mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-08-29 11:37:42 +02:00
Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Thiago PadilhaandGeorgi Gerganov
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f91123d2d0 | ||
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f5e85d43a0 | ||
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511f9c1379 | ||
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Xuan-Son NguyenandGitHub
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b19cbe925b | ||
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Ozymandias_EBONandGitHub
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d077b4c214 | ||
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TitaniumtownandGitHub
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be876204aa | ||
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Georgi GerganovandGitHub
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8963a9bdcd |
@@ -1,12 +1,12 @@
|
||||
ARG OPENVINO_VERSION_MAJOR=2026.3
|
||||
ARG OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c
|
||||
ARG OPENVINO_VERSION_MAJOR=2026.3.1
|
||||
ARG OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d
|
||||
ARG UBUNTU_VERSION=24.04
|
||||
|
||||
# Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
|
||||
ARG IGC_VERSION=v2.38.2
|
||||
ARG IGC_VERSION_FULL=2_2.38.2+22051
|
||||
ARG COMPUTE_RUNTIME_VERSION=26.27.39122.11
|
||||
ARG COMPUTE_RUNTIME_VERSION_FULL=26.27.39122.11-0
|
||||
ARG IGC_VERSION=v2.40.13
|
||||
ARG IGC_VERSION_FULL=2_2.40.13+22418
|
||||
ARG COMPUTE_RUNTIME_VERSION=26.31.39395.13
|
||||
ARG COMPUTE_RUNTIME_VERSION_FULL=26.31.39395.13-0
|
||||
ARG IGDGMM_VERSION=22.10.0
|
||||
|
||||
# Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
|
||||
|
||||
@@ -41,8 +41,8 @@ jobs:
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||||
|
||||
env:
|
||||
# Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
@@ -69,8 +69,8 @@ jobs:
|
||||
|
||||
env:
|
||||
# Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
|
||||
@@ -32,6 +32,8 @@ env:
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LLAMA_ARG_LOG_COLORS: 1
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LLAMA_ARG_LOG_PREFIX: 1
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LLAMA_ARG_LOG_TIMESTAMPS: 1
|
||||
# TODO: fix and re-enable the `test-llama-archs` and `test-recurrent-state-rollback`
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CTEST_EXCLUDE: "test-llama-archs|^test-recurrent-state-rollback"
|
||||
|
||||
jobs:
|
||||
ubuntu-24-openvino:
|
||||
@@ -39,8 +41,8 @@ jobs:
|
||||
|
||||
env:
|
||||
# Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
@@ -78,26 +80,24 @@ jobs:
|
||||
|
||||
- name: Test (CPU)
|
||||
id: cmake_test_cpu
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||||
# TODO: fix and re-enable the `test-llama-archs` test below
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run: |
|
||||
cd ${{ github.workspace }}
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||||
ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 2000
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||||
ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000
|
||||
|
||||
- name: Test (GPU)
|
||||
id: cmake_test_gpu
|
||||
# TODO: fix and re-enable the `test-llama-archs` test below
|
||||
run: |
|
||||
cd ${{ github.workspace }}
|
||||
export GGML_OPENVINO_DEVICE=GPU
|
||||
ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 3000
|
||||
ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000
|
||||
|
||||
openvino-windows-2022:
|
||||
runs-on: windows-2022
|
||||
|
||||
env:
|
||||
# Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
@@ -159,14 +159,13 @@ jobs:
|
||||
- name: Test (CPU)
|
||||
id: cmake_test_cpu
|
||||
shell: cmd
|
||||
# TODO: fix and re-enable the `test-llama-archs` test below
|
||||
run: |
|
||||
REM Find extracted OpenVINO folder dynamically
|
||||
for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
|
||||
call "%OPENVINO_ROOT%\setupvars.bat"
|
||||
|
||||
cd build
|
||||
ctest --test-dir ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" -C Release --verbose --timeout 3000
|
||||
ctest --test-dir ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" -C Release --verbose --timeout 3000
|
||||
|
||||
- name: ccache-clear
|
||||
uses: ./.github/actions/ccache-clear
|
||||
|
||||
@@ -288,8 +288,8 @@ jobs:
|
||||
|
||||
env:
|
||||
# Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
|
||||
@@ -415,8 +415,8 @@ jobs:
|
||||
|
||||
env:
|
||||
# Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Set OpenVINO version output
|
||||
@@ -529,8 +529,8 @@ jobs:
|
||||
|
||||
env:
|
||||
# Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
|
||||
OPENVINO_VERSION_MAJOR: "2026.3"
|
||||
OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR: "2026.3.1"
|
||||
OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
|
||||
|
||||
steps:
|
||||
- name: Set OpenVINO version output
|
||||
|
||||
@@ -189,8 +189,8 @@ if [ ! -z ${GG_BUILD_OPENVINO} ]; then
|
||||
fi
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON"
|
||||
|
||||
# TODO: fix and re-enable the `test-llama-archs` test below
|
||||
CTEST_EXTRA="-E test-llama-archs|test-recurrent-state-rollback-nemotron-h"
|
||||
# TODO: fix and re-enable the `test-llama-archs` and `test-recurrent-state-rollback*`
|
||||
CTEST_EXTRA="-E test-llama-archs|^test-recurrent-state-rollback"
|
||||
fi
|
||||
|
||||
## helpers
|
||||
|
||||
+46
-40
@@ -22,8 +22,8 @@ The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a t
|
||||
- [0. Prerequisites](#0-prerequisites)
|
||||
- [1. Install OpenVINO Runtime](#1-install-openvino-runtime)
|
||||
- [2. Build llama.cpp with OpenVINO Backend](#2-build-llamacpp-with-openvino-backend)
|
||||
- [Automated Ubuntu Build Script](#automated-ubuntu-build-script)
|
||||
- [Automated Windows Build Script](#automated-windows-build-script)
|
||||
- [Ubuntu Build Script](#ubuntu-build-script)
|
||||
- [Windows Build Script](#windows-build-script)
|
||||
- [3. Download Sample Model](#3-download-sample-model)
|
||||
- [4. Run Inference with OpenVINO Backend](#4-run-inference-with-openvino-backend)
|
||||
- [5. Docker Build](#5-docker-build)
|
||||
@@ -96,7 +96,7 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
|
||||
- **SL** = Stateless (`GGML_OPENVINO_STATEFUL_EXECUTION=0`)
|
||||
- **SF** = Stateful (`GGML_OPENVINO_STATEFUL_EXECUTION=1`)
|
||||
- Note: The NPU operates in stateless mode only.
|
||||
- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.18.38308.1 | Intel NPU Driver 1.33.0.
|
||||
- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.31.39395.13-0 | Intel NPU Driver 1.35.0.
|
||||
- See [Known Limitations](#known-limitations) for context on observed failures.
|
||||
|
||||
| Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
|
||||
@@ -105,27 +105,32 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
|
||||
| [bartowski/Llama-3.2-3B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/Meta-Llama-3.1-8B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| | | | |
|
||||
| [Qwen/qwen2.5-1.5b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [Qwen/qwen2.5-coder-7b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [bartowski/Qwen_Qwen3-0.6B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-0.6B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [Qwen/qwen2.5-1.5b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [Qwen/qwen2.5-coder-7b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/Qwen_Qwen3-0.6B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-0.6B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/Qwen_Qwen3.5-0.8B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-0.8B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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||||
| [bartowski/Qwen_Qwen3.5-2B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-2B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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||||
| [bartowski/Qwen_Qwen3.5-4B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-4B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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||||
| [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
|
||||
| | | | |
|
||||
| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
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||||
| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
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||||
| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
|
||||
| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
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||||
| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
|
||||
| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
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||||
| | | | |
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||||
| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
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||||
| [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
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||||
| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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||||
| [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/microsoft_Phi-4-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/microsoft_Phi-4-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| | | | |
|
||||
| [bartowski/Mistral-7B-Instruct-v0.3-Q4_K_M](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [QuantFactory/Ministral-3b-instruct.Q4_K_M](https://huggingface.co/QuantFactory/Ministral-3b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/Ministral-8B-Instruct-2410-Q4_K_M](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| | | | |
|
||||
| [bartowski/DeepSeek-R1-Distill-Llama-8B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| | | | |
|
||||
| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✗ / ✗ | ✓ |
|
||||
| [ibm-granite/granite-4.0-micro-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-micro-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
@@ -133,10 +138,10 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
|
||||
| [ibm-research/granite-3.2-8b-instruct-Q4_K_M](https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| | | | |
|
||||
| [HuggingFaceTB/smollm2-1.7b-instruct-q4_k_m](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [openbmb/MiniCPM-V-2_6-Q4_K_M](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [bartowski/tencent_Hunyuan-7B-Instruct-Q4_K_M](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-Q4_K_M](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [bartowski/prism-ml_Bonsai-8B-unpacked-Q4_K_M](https://huggingface.co/bartowski/prism-ml_Bonsai-8B-unpacked-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
|
||||
| [openbmb/MiniCPM-V-2_6-Q4_K_M](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/tencent_Hunyuan-7B-Instruct-Q4_K_M](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-Q4_K_M](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
|
||||
| [bartowski/prism-ml_Bonsai-8B-unpacked-Q4_K_M](https://huggingface.co/bartowski/prism-ml_Bonsai-8B-unpacked-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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||||
| | | | |
|
||||
| [gpustack/bge-m3-Q4_K_M.gguf](https://huggingface.co/gpustack/bge-m3-GGUF) | ✓ | ✗ | ✗ |
|
||||
|
||||
@@ -217,18 +222,18 @@ cmake --build build\ReleaseOV --parallel
|
||||
> [!NOTE]
|
||||
> The Windows install path is `C:\Intel\openvino` (no spaces) to avoid quoting problems some CMake/Ninja toolchains have with `C:\Program Files (x86)\...`. Adjust to wherever you installed OpenVINO Runtime. From `cmd`, run `C:\Intel\openvino\setupvars.bat`; from PowerShell, run `& "C:\Intel\openvino\setupvars.ps1"` instead. Once the build is finished you can launch the binaries from any `cmd` or `PowerShell` window after sourcing the matching `setupvars` script for that shell.
|
||||
|
||||
#### Automated Ubuntu Build Script
|
||||
#### Ubuntu Build Script
|
||||
|
||||
For Ubuntu24 users, the following shell script automates the prerequisite installs (build tools, OpenCL ICD), the OpenVINO Runtime download/extract/setup, and the Ninja-based llama.cpp build.
|
||||
Save the following as `ubuntu-llamacpp-ov-install.sh` next to where you want the `llama.cpp` folder to land, then run it:
|
||||
Save the following as `build-llamacpp-ov.sh` next to where you want the `llama.cpp` folder to land, then run it:
|
||||
|
||||
```bash
|
||||
chmod +x ubuntu-llamacpp-ov-install.sh
|
||||
./ubuntu-llamacpp-ov-install.sh
|
||||
chmod +x build-llamacpp-ov.sh
|
||||
./build-llamacpp-ov.sh
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Click to expand <code>ubuntu-llamacpp-ov-install.sh</code></summary>
|
||||
<summary>Click to expand <code>build-llamacpp-ov.sh</code></summary>
|
||||
|
||||
```bash
|
||||
#!/usr/bin/env bash
|
||||
@@ -237,8 +242,8 @@ chmod +x ubuntu-llamacpp-ov-install.sh
|
||||
# ============================================
|
||||
set -euo pipefail
|
||||
|
||||
OPENVINO_VERSION_MAJOR="2026.3"
|
||||
OPENVINO_VERSION_FULL="2026.3.0.22451.bd8d6542e3c"
|
||||
OPENVINO_VERSION_MAJOR="2026.3.1"
|
||||
OPENVINO_VERSION_FULL="2026.3.1.22476.56d9685302d"
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
|
||||
@@ -313,8 +318,9 @@ fi
|
||||
echo "============================================"
|
||||
echo "Configuring with CMake..."
|
||||
echo "============================================"
|
||||
# shellcheck disable=SC1091
|
||||
set +u
|
||||
source "${OPENVINO_ROOT}/setupvars.sh"
|
||||
set -u
|
||||
|
||||
cmake -B build/ReleaseOV -G Ninja \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
@@ -334,27 +340,27 @@ echo " ./build/ReleaseOV/bin/llama-cli -m model.gguf"
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> The script pins OpenVINO `2026.3` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
|
||||
> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
|
||||
|
||||
</details>
|
||||
|
||||
#### Automated Windows Build Script
|
||||
#### Windows Build Script
|
||||
|
||||
For Windows users, the following `.bat` script automates the prerequisite installs (Git, Ninja, CMake, Visual Studio 2022 Build Tools, vcpkg + OpenCL), the OpenVINO Runtime download/extract, and the Ninja-based llama.cpp build.
|
||||
Save the following as `windows-llamacpp-ov-install.bat` next to where you want the `llama.cpp` to land, then run it from either **Command Prompt** or **PowerShell**:
|
||||
Save the following as `build-llamacpp-ov.bat` next to where you want the `llama.cpp` to land, then run it from either **Command Prompt** or **PowerShell**:
|
||||
|
||||
```cmd
|
||||
:: Command Prompt
|
||||
windows-llamacpp-ov-install.bat
|
||||
build-llamacpp-ov.bat
|
||||
```
|
||||
|
||||
```powershell
|
||||
# PowerShell
|
||||
.\windows-llamacpp-ov-install.bat
|
||||
.\build-llamacpp-ov.bat
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Click to expand <code>windows-llamacpp-ov-install.bat</code></summary>
|
||||
<summary>Click to expand <code>build-llamacpp-ov.bat</code></summary>
|
||||
|
||||
```bat
|
||||
@echo off
|
||||
@@ -364,8 +370,8 @@ REM ============================================
|
||||
REM llama.cpp OpenVINO Build Script (Ninja)
|
||||
REM ============================================
|
||||
|
||||
set "OPENVINO_VERSION_MAJOR=2026.3"
|
||||
set "OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c"
|
||||
set "OPENVINO_VERSION_MAJOR=2026.3.1"
|
||||
set "OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d"
|
||||
|
||||
set "SCRIPT_DIR=%~dp0"
|
||||
set "VCPKG_DIR=C:\vcpkg"
|
||||
@@ -453,9 +459,6 @@ if exist "%OPENVINO_INSTALL_DIR%\setupvars.bat" (
|
||||
)
|
||||
|
||||
REM Move the single top-level folder contents into the versioned install dir.
|
||||
REM NOTE: delayed expansion (!VAR!) is required because the surrounding else( ... )
|
||||
REM block is parsed once up-front, so %OPENVINO_EXTRACTED% would expand to "" here
|
||||
REM and xcopy would then treat "\*" as C:\* and fail with "Cannot perform a cyclic copy".
|
||||
set "OPENVINO_EXTRACTED="
|
||||
for /d %%i in ("%OPENVINO_EXTRACT_TMP%\*") do set "OPENVINO_EXTRACTED=%%i"
|
||||
if not defined OPENVINO_EXTRACTED (
|
||||
@@ -547,7 +550,7 @@ endlocal
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> The script pins OpenVINO `2026.3` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
|
||||
> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
|
||||
|
||||
</details>
|
||||
|
||||
@@ -712,6 +715,7 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
|
||||
| `GGML_OPENVINO_CACHE_DIR` | String | `not set` | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
|
||||
| `GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR` | String | `not set` | Directory for the frontend compiled-model cache. When set, OpenVINO compiled models are exported as blobs and imported on later runs to skip weight requantization, graph conversion, and compilation for matching single-graph models. |
|
||||
| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| Integer | `256` | Token chunk size for **NPU** prefill (NPU-only; ignored on CPU/GPU). Must be a positive integer; otherwise the default is used. |
|
||||
| `GGML_OPENVINO_NPU_COMPILE_CONFIG` | String | `not set` | NPU-only compiler mode parameters forwarded to OpenVINO as `NPU_COMPILATION_MODE_PARAMS`, for example `optimization-level=3`. |
|
||||
| `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean | `0` | Enable stateful KV cache for better performance. Recommended on CPU, GPU. |
|
||||
| `GGML_OPENVINO_DISABLE_CACHE` | Boolean | `0` | Disable the in-process compiled-model / decoder cache (cache is on by default). Set to `1` to disable. |
|
||||
| `GGML_OPENVINO_DISABLE_KV_SLICE` | Boolean | `0` | Disable the KV-cache input-tensor slicing optimization (slicing is on by default on CPU/GPU). Set to `1` to disable. |
|
||||
@@ -725,9 +729,11 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
|
||||
| `GGML_OPENVINO_DEBUG_INPUT` | Boolean | `0` | Enable input debugging and print input tensor info. |
|
||||
| `GGML_OPENVINO_DEBUG_OUTPUT` | Boolean | `0` | Enable output debugging and print output tensor info. |
|
||||
| `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | Boolean | `0` | Print tensor address map once. |
|
||||
| `GGML_OPENVINO_LOG_UNSUPPORTED_OPS`| Boolean | `0` | Log warning messages with tensor details and rejection reasons for any ops not supported by the OpenVINO backend. Emits at `WARN` level (requires `--log-verbosity >= 2`, enabled by default). |
|
||||
|
||||
> [!NOTE]
|
||||
>`GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
|
||||
> - `GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
|
||||
> - `GGML_OPENVINO_LOG_UNSUPPORTED_OPS` emits logs at `WARN` level (`GGML_LOG_WARN`), which requires application log verbosity `--log-verbosity >= 2` (or `-lv 2`).
|
||||
|
||||
### Example Usage
|
||||
|
||||
|
||||
@@ -800,7 +800,7 @@ void ggml_metal_encoder_set_buffer(ggml_metal_encoder_t encoder, struct ggml_met
|
||||
}
|
||||
|
||||
void ggml_metal_encoder_set_threadgroup_memory_size(ggml_metal_encoder_t encoder, size_t size, int idx) {
|
||||
[encoder->obj setThreadgroupMemoryLength:size atIndex:idx];
|
||||
[encoder->obj setThreadgroupMemoryLength:GGML_PAD(size, 16) atIndex:idx];
|
||||
}
|
||||
|
||||
void ggml_metal_encoder_dispatch_threadgroups(ggml_metal_encoder_t encoder, int tg0, int tg1, int tg2, int tptg0, int tptg1, int tptg2) {
|
||||
|
||||
@@ -66,6 +66,7 @@ fa_vec_cfg_t fa_vec_baseline_cfg(int dk, int dv) {
|
||||
// One row per kept bucket, plus per-(dtype,dk,dv) ne11-collapsed domain defaults
|
||||
// (ne11_b = FA_VEC_NE11_DEFAULT, ne01_b = domain). To retune or add a device, re-run the
|
||||
// sweep and paste its output. See ggml-metal-tuning.h for the row/lookup semantics.
|
||||
// ref: https://github.com/ggml-org/llama.cpp/pull/27824
|
||||
constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
|
||||
{ { GGML_METAL_DEVICE_M1_PRO, GGML_TYPE_F16, 32, 32, 3, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M1_PRO, GGML_TYPE_F16, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
@@ -448,6 +449,325 @@ constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
|
||||
{ { GGML_METAL_DEVICE_M2_ULTRA, GGML_TYPE_Q8_0, 512, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M2_ULTRA, GGML_TYPE_Q8_0, 576, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M2_ULTRA, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 32, 32, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, 3, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, 3, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, 3, 3 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 96, 96, 3, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 128, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 128, 128, 1, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 128, 128, 2, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 192, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 192, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 192, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 128, 1, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 128, 1, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 256, 256, -1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 256, 256, 2, 3 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 320, 256, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 320, 256, 3, 0 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 2, 0 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 3, 0 }, { 4, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 1, 3 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 2, 1 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 2, 3 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 3, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 512, 512, 3, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 2, 0 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 2, 3 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_F16, 576, 512, 3, 1 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 1, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 64, 64, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 96, 96, 3, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 128, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 192, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 512, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 512, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 576, 512, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 576, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M3_MAX, GGML_TYPE_Q8_0, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 2, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 32, 32, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 64, 64, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 64, 64, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 96, 96, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 96, 96, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 128, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 128, 128, 1, 2 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 128, 128, 1, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 128, 128, 2, 2 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 128, 128, 2, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 192, 3, 0 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 192, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 192, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 128, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 192, 128, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 256, 256, -1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 256, 256, 3, 0 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 320, 256, 3, 0 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 512, 512, 3, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 512, 512, 3, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 512, 512, 3, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_F16, 512, 512, 3, 3 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 128, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 192, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 512, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 512, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 1, 3 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_0, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 128, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 192, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 1, 3 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q4_1, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 64, 64, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 64, 64, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 64, 64, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 64, 64, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 96, 96, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 96, 96, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 96, 96, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 96, 96, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 128, 128, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 256, 256, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 256, 256, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 320, 256, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 320, 256, 2, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 320, 256, 3, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 512, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 512, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 576, 512, 2, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 576, 512, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 576, 512, 2, 3 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_0, 576, 512, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 64, 64, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 64, 64, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 64, 64, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 64, 64, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 96, 96, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 96, 96, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 96, 96, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 96, 96, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, 1, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 128, 128, 3, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 256, 256, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 256, 256, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 320, 256, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 320, 256, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 320, 256, 2, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 320, 256, 3, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 512, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 512, 512, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 576, 512, 2, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 576, 512, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 576, 512, 2, 3 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q5_1, 576, 512, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 192, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 512, 512, -1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 512, 512, -1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 576, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 576, 512, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_F16, 32, 32, 1, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_F16, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_F16, 32, 32, 2, 3 }, { 2, 4 } },
|
||||
@@ -508,6 +828,7 @@ constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_Q8_0, 576, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_Q8_0, 576, 512, 1, 3 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M4_PRO, GGML_TYPE_Q8_0, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M4_MAX, GGML_TYPE_F16, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4_MAX, GGML_TYPE_F16, 32, 32, 2, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4_MAX, GGML_TYPE_F16, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
@@ -699,7 +1020,215 @@ constexpr fa_vec_entry_t fa_vec_tuned_table[] = {
|
||||
{ { GGML_METAL_DEVICE_M4_MAX, GGML_TYPE_Q8_0, 576, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M4_MAX, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M5_MAX, GGML_TYPE_F16, 32, 32, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 32, 32, 3, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 1, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 64, 64, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 96, 96, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 96, 96, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 3, 0 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 1, 2 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 1, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 3, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 128, 128, 3, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 192, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 192, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 192, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 3, 2 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 192, 128, 3, 3 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 256, 256, -1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 256, 256, 1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 256, 256, 1, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 320, 256, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 320, 256, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 320, 256, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 320, 256, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 2, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 2, 3 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 3, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 3, 2 }, { 4, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 3, 3 }, { 4, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 512, 512, 3, 4 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 576, 512, 2, 0 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 576, 512, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 576, 512, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_F16, 576, 512, 2, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 32, 32, 3, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 96, 96, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 96, 96, 3, 4 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 192, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 192, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 192, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 128, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 320, 256, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 512, 512, 3, 0 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 512, 512, 2, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 512, 512, 3, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 576, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 2, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 32, 32, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 64, 64, 3, 4 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 128, 128, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 128, 128, 1, 4 }, { 1, 1 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 192, 192, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 192, 192, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 192, 128, 2, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 192, 128, 3, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 256, 256, -1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 256, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 256, 256, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 320, 256, 3, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 320, 256, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 320, 256, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 512, 512, 2, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 512, 512, 2, 3 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 512, 512, 3, 3 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, -1, 1 }, { 2, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, 1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, 1, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, 1, 3 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, 1, 4 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_F16, 576, 512, 2, 2 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 32, 32, -1, 1 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 32, 32, 1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 32, 32, 1, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 32, 32, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 32, 32, 3, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 64, 64, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 96, 96, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, 1, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, 2, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, 2, 3 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, 2, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 192, 3, 4 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 192, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 512, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 512, 512, 1, 0 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 512, 512, -1, 1 }, { 1, 2 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 512, 512, 2, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q4_0, 512, 512, 2, 3 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 1, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 1, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 2, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 2, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 32, 32, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 64, 64, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 64, 64, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 64, 64, 3, 2 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 64, 64, 3, 3 }, { 4, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 96, 96, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 96, 96, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 96, 96, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 128, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 128, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 128, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 128, 128, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 192, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 192, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 192, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 192, 2, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 192, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 128, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 128, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 128, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 192, 128, 3, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 256, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 256, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 320, 256, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 320, 256, -1, 1 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 576, 512, -1, 0 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_PRO, GGML_TYPE_Q8_0, 576, 512, -1, 1 }, { 1, 4 } },
|
||||
|
||||
{ { GGML_METAL_DEVICE_M5_MAX, GGML_TYPE_F16, 32, 32, -1, 1 }, { 2, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_MAX, GGML_TYPE_F16, 32, 32, 1, 2 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_MAX, GGML_TYPE_F16, 32, 32, 1, 4 }, { 1, 4 } },
|
||||
{ { GGML_METAL_DEVICE_M5_MAX, GGML_TYPE_F16, 32, 32, 2, 2 }, { 4, 4 } },
|
||||
|
||||
@@ -435,7 +435,7 @@ kernel void kernel_mul_mm_id(
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
|
||||
|
||||
@@ -1,6 +1,8 @@
|
||||
find_package(OpenVINO REQUIRED COMPONENTS Runtime Threading)
|
||||
find_package(OpenCL REQUIRED)
|
||||
|
||||
message(STATUS "Found OpenVINO: ${OpenVINO_DIR} (found version \"${OpenVINO_VERSION}\")")
|
||||
|
||||
file(GLOB_RECURSE GGML_HEADERS_OPENVINO "*.h" "*.hpp")
|
||||
file(GLOB_RECURSE GGML_SOURCES_OPENVINO "*.cpp")
|
||||
|
||||
|
||||
@@ -357,6 +357,18 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
|
||||
break;
|
||||
}
|
||||
case GGML_OP_VIEW: {
|
||||
if (m_is_static && node->src[0] != nullptr &&
|
||||
(node->src[0]->op == GGML_OP_GATED_DELTA_NET || node->src[0]->op == GGML_OP_CONCAT)) {
|
||||
// VIEW slicing a GATED_DELTA_NET combined [attn|state] output, or the conv_input
|
||||
// CONCAT. The consuming CPY/RMS_NORM op recovers the true window at runtime via
|
||||
// ssm_state_size / the fixed conv kernel width, so this VIEW must stay an identity
|
||||
// pass-through of the full source here too (it already is on the dynamic path);
|
||||
// otherwise the generic static-mode Slice below would bake in the *captured*
|
||||
// cgraph's token count, which is wrong once the compiled static model runs with a
|
||||
// different token count (prefill chunk size or 1).
|
||||
op_case = 1;
|
||||
break;
|
||||
}
|
||||
if (node->src[0]->op == GGML_OP_VIEW) {
|
||||
auto * src = node->src[0];
|
||||
if (ggml_nelements(node) != ggml_nelements(src)) {
|
||||
@@ -408,6 +420,23 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_POOL_2D: {
|
||||
const ggml_op_pool pool_mode = static_cast<ggml_op_pool>(node->op_params[0]);
|
||||
switch (pool_mode) {
|
||||
case GGML_OP_POOL_MAX: {
|
||||
op_case = 1;
|
||||
break;
|
||||
}
|
||||
case GGML_OP_POOL_AVG: {
|
||||
op_case = 2;
|
||||
break;
|
||||
}
|
||||
default:
|
||||
op_case = 0;
|
||||
break;
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_CPY: {
|
||||
if (node->src[0]->op == GGML_OP_VIEW) {
|
||||
if (node->src[0]->src[0]->op == GGML_OP_GATED_DELTA_NET) {
|
||||
@@ -425,6 +454,31 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
|
||||
is_kvcache(node->src[1]->view_src, nullptr)) {
|
||||
// s_copy defrag remainder writeback: gathered extra state rows copied back into the cache
|
||||
op_case = 3;
|
||||
} else if (node->src[1] != nullptr && node->src[1]->op == GGML_OP_VIEW && node->src[1]->view_src != nullptr) {
|
||||
// op_case 5: KV write for decoder self-attention (dynamic write offset)
|
||||
// op_case 6: KV write for encoder self-attn or cross-attn (static offset)
|
||||
const ggml_tensor * kv_buf = node->src[1]->view_src;
|
||||
if (kv_buf->ne[1] == 1 && kv_buf->ne[2] == 1 && kv_buf->ne[3] == 1) {
|
||||
op_case = 6;
|
||||
// Forward-scan the graph for a FLASH_ATTN_EXT that reads from
|
||||
// the same buffer. Having a mask (src[3] != nullptr) implies
|
||||
// decoder self-attention and the write offset is dynamic.
|
||||
for (int i = 0; i < m_cgraph->n_nodes; i++) {
|
||||
const ggml_tensor * n = m_cgraph->nodes[i];
|
||||
if (n->op != GGML_OP_FLASH_ATTN_EXT) {
|
||||
continue;
|
||||
}
|
||||
// K (src[1]) and V (src[2]) are 3-D views whose view_src is
|
||||
// the flat KV buffer we are writing to.
|
||||
if ((n->src[1] != nullptr && n->src[1]->view_src == kv_buf) ||
|
||||
(n->src[2] != nullptr && n->src[2]->view_src == kv_buf)) {
|
||||
if (n->src[3] != nullptr) {
|
||||
op_case = 5; // decoder self-attention: mask present
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -448,6 +502,15 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_FLASH_ATTN_EXT: {
|
||||
if (node->src[1] != nullptr && node->src[1]->op == GGML_OP_VIEW && node->src[1]->view_src != nullptr) {
|
||||
const ggml_tensor * kv_buf = node->src[1]->view_src;
|
||||
if (kv_buf->ne[1] == 1 && kv_buf->ne[2] == 1 && kv_buf->ne[3] == 1) {
|
||||
op_case = (node->src[3] != nullptr) ? 1 : 2;
|
||||
}
|
||||
}
|
||||
break;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
@@ -479,23 +542,35 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
|
||||
|
||||
switch (node->op) {
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
if (node->src[0] == nullptr || node->src[1] == nullptr || node->src[3] == nullptr) {
|
||||
if (node->src[0] == nullptr || node->src[1] == nullptr) {
|
||||
return -1;
|
||||
}
|
||||
switch (node->src[1]->op) {
|
||||
case GGML_OP_PERMUTE:
|
||||
// case 0: node op is FLASH_ATTN_EXT, src 1 not null & op is PERMUTE & the permuted tensor src is the view of cache k
|
||||
if (node->src[1]->src[0] != nullptr && node->src[1]->src[0]->op == GGML_OP_VIEW) {
|
||||
// case 0: src[1] is PERMUTE of a cache VIEW, mask required
|
||||
if (node->src[3] != nullptr && node->src[1]->src[0] != nullptr &&
|
||||
node->src[1]->src[0]->op == GGML_OP_VIEW) {
|
||||
return 0;
|
||||
}
|
||||
break;
|
||||
case GGML_OP_CPY:
|
||||
// case 1: node op is FLASH_ATTN_EXT, src 1 not null & op is CPY & the copied tensor src is PERMUTE & the permuted tensor src is the view of cache k
|
||||
if (node->src[1]->src[0] != nullptr && node->src[1]->src[0]->op == GGML_OP_PERMUTE &&
|
||||
node->src[1]->src[0]->src[0] != nullptr && node->src[1]->src[0]->src[0]->op == GGML_OP_VIEW) {
|
||||
// case 1: src[1] is CPY of a PERMUTE(VIEW), mask required
|
||||
if (node->src[3] != nullptr && node->src[1]->src[0] != nullptr &&
|
||||
node->src[1]->src[0]->op == GGML_OP_PERMUTE && node->src[1]->src[0]->src[0] != nullptr &&
|
||||
node->src[1]->src[0]->src[0]->op == GGML_OP_VIEW) {
|
||||
return 1;
|
||||
}
|
||||
break;
|
||||
case GGML_OP_VIEW:
|
||||
// cases 4/5/6: whisper - K is a direct non-contiguous VIEW_3D of a KV cache
|
||||
if (node->src[1]->view_src != nullptr) {
|
||||
if (node->src[3] != nullptr) {
|
||||
return 4; // decoder self-attention
|
||||
} else {
|
||||
return 5; // cross-attention or encoder self-attention
|
||||
};
|
||||
}
|
||||
break;
|
||||
default:
|
||||
break;
|
||||
}
|
||||
@@ -548,6 +623,18 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
|
||||
cache_k_permute = node->src[0]->src[0]->src[0];
|
||||
mask = node->src[1];
|
||||
break;
|
||||
case 4:
|
||||
case 5: {
|
||||
// whisper: K is a direct VIEW_3D of the KV buffer, no PERMUTE node
|
||||
auto * cache_k_view = node->src[1]; // VIEW_3D of kv_self.k or kv_cross.k`
|
||||
compute_params.token_len_per_seq = node->src[0]->ne[1];
|
||||
if (attention_pattern_case == 4) {
|
||||
compute_params.attention_size = cache_k_view->ne[1];
|
||||
} else {
|
||||
compute_params.attention_size_static = cache_k_view->ne[1];
|
||||
}
|
||||
continue;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
@@ -654,10 +741,8 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
|
||||
ComputeParams::RsWriteback writeback;
|
||||
writeback.slot_begin = (int) (dest_view->view_offs / row_bytes);
|
||||
if (is_conv) {
|
||||
// conv_input column the copied window starts at
|
||||
writeback.src_begin = (int) (node->src[0]->view_offs / node->src[0]->view_src->nb[0]);
|
||||
} else if (is_gdn) {
|
||||
// first row of the state part of the gated-delta-net output
|
||||
writeback.src_begin = (int) (node->src[0]->view_offs / node->src[0]->view_src->nb[1]);
|
||||
}
|
||||
compute_params.rs_writebacks[get_tensor_ov_name(cgraph, node)] = writeback;
|
||||
@@ -718,11 +803,15 @@ ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op,
|
||||
} else if (is_kvcache(input, op)) {
|
||||
// kvcache
|
||||
input_shape = ov::PartialShape{get_shape(input)};
|
||||
if (!m_is_static) {
|
||||
// Whisper.cpp uses a fixed size 1D KV buffer [N, 1, 1, 1] (GGML) or [1, 1, 1, N] (OV).
|
||||
// the token fill level is handled by token_len_per_seq + dynamic mask input.
|
||||
// skip dynamic dim and stateful reshape for this layout.
|
||||
const bool is_flat_kv = (input->ne[1] == 1 && input->ne[2] == 1 && input->ne[3] == 1);
|
||||
if (!m_is_static && !is_flat_kv) {
|
||||
// do not fix ctx size to make llama-bench work across test params
|
||||
input_shape[2] = -1;
|
||||
}
|
||||
if (is_stateful()) {
|
||||
if (is_stateful() && !is_flat_kv) {
|
||||
// Convert stateless KV cache layout [1, 1, seq, n_heads_kv * head_size]
|
||||
// to stateful layout [1, seq, n_heads_kv, head_size].
|
||||
assert(input_shape.size() == 4 && input_shape[0] == 1 && input_shape[1] == 1 &&
|
||||
@@ -738,7 +827,9 @@ ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op,
|
||||
input_shape = ov::PartialShape{1, 1, 1, len};
|
||||
|
||||
} else if (is_inp_s_copy(input, op) || is_s_copy_leaf(input)) {
|
||||
input_shape = ov::PartialShape{1, 1, 1, -1};
|
||||
// On NPU the total slot count (n_seq_max) is fixed at translation time, so the s_copy
|
||||
// index list has a static length; on CPU/GPU it may change across compiles (defrag).
|
||||
input_shape = m_is_static ? ov::PartialShape{get_shape(input)} : ov::PartialShape{1, 1, 1, -1};
|
||||
|
||||
} else {
|
||||
input_shape = ov::PartialShape{get_shape(input)};
|
||||
@@ -790,13 +881,16 @@ void GgmlOvDecoder::add_extra_inputs() {
|
||||
// see llama_kv_cache_unified::get_n_kv and llama_kv_cache_unified::get_padding.
|
||||
// 2. `n_seq_active` and `seq_active_start`, used in FLASH_ATTN_EXT to indicate the active sequences in the batch
|
||||
|
||||
auto create_1d_input = [this](const std::string & name, int64_t value) {
|
||||
m_model_extra_inputs[name] = {ov::element::i64, ov::Shape{1}, value, !m_is_static};
|
||||
auto create_1d_input = [this](const std::string & name, int64_t value, bool force_parameter = false) {
|
||||
m_model_extra_inputs[name] = {ov::element::i64, ov::Shape{1}, value, force_parameter || !m_is_static};
|
||||
};
|
||||
|
||||
if (m_compute_params.attention_size != -1) {
|
||||
create_1d_input("attention_size", m_compute_params.attention_size);
|
||||
}
|
||||
if (m_compute_params.attention_size_static != -1) {
|
||||
create_1d_input("attention_size_static", m_compute_params.attention_size_static);
|
||||
}
|
||||
if (m_compute_params.attention_size_swa != -1) {
|
||||
create_1d_input("attention_size_swa", m_compute_params.attention_size_swa);
|
||||
}
|
||||
@@ -809,17 +903,32 @@ void GgmlOvDecoder::add_extra_inputs() {
|
||||
// create_1d_input("token_len", m_compute_params.token_len_per_seq * m_compute_params.n_seq_active);
|
||||
|
||||
if (m_compute_params.cache_rs_reset_idx != -1) {
|
||||
create_1d_input("cache_rs_reset_idx", m_compute_params.cache_rs_reset_idx);
|
||||
create_1d_input("cache_rs_reset_len", m_compute_params.cache_rs_reset_len);
|
||||
// Whether/which cache slot to reset varies per compute call (e.g. a new sequence starting
|
||||
// vs. continued decoding). can_reuse_statically() does not invalidate the cached static
|
||||
// model on ComputeParams changes, so these must stay runtime Parameters even when static
|
||||
// (scale.cpp op_case 1 only uses them in value comparisons, never as Slice bounds, so this
|
||||
// does not reintroduce dynamic shapes).
|
||||
create_1d_input("cache_rs_reset_idx", m_compute_params.cache_rs_reset_idx, /*force_parameter=*/true);
|
||||
create_1d_input("cache_rs_reset_len", m_compute_params.cache_rs_reset_len, /*force_parameter=*/true);
|
||||
}
|
||||
|
||||
if (m_compute_params.s_copy_active_slot_len != -1) {
|
||||
create_1d_input("s_copy_active_slot_len", m_compute_params.s_copy_active_slot_len);
|
||||
if (m_is_static) {
|
||||
// Number of real tokens in the current prefill chunk. The last chunk is padded with
|
||||
// fabricated token ids; attention masks them out, but the recurrent (GDN/conv) path
|
||||
// would otherwise fold them into cache_r/cache_s permanently. Varies per chunk, so it
|
||||
// must stay a runtime Parameter; it is only compared against a Range or used as Gather
|
||||
// indices, so it does not make any shape dynamic.
|
||||
create_1d_input("chunk_valid_len", get_static_n_tokens(), /*force_parameter=*/true);
|
||||
}
|
||||
}
|
||||
|
||||
for (const auto & [node_name, writeback] : m_compute_params.rs_writebacks) {
|
||||
create_1d_input("rs_slot_begin_" + node_name, writeback.slot_begin);
|
||||
create_1d_input("rs_src_begin_" + node_name, writeback.src_begin);
|
||||
if (!m_is_static) {
|
||||
create_1d_input("rs_src_begin_" + node_name, writeback.src_begin);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1785,13 +1894,23 @@ void GgmlOvDecoder::compute_node_dynamic_dims() {
|
||||
auto dynamic_dim_stride = src_logical_nb[dynamic_dim_idx] / ggml_type_size(node->src[0]->type) *
|
||||
ggml_type_size(node->type);
|
||||
int matched_dim_count = 0;
|
||||
int first_matched_dim = -1;
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
if (node->nb[i] == dynamic_dim_stride && node->ne[i] == node->src[0]->ne[dynamic_dim_idx]) {
|
||||
if (first_matched_dim == -1) {
|
||||
first_matched_dim = i;
|
||||
}
|
||||
m_node_dynamic_dims[node] = i;
|
||||
matched_dim_count++;
|
||||
}
|
||||
}
|
||||
if (matched_dim_count != 1) {
|
||||
if (matched_dim_count > 1 && node->src[0]->ne[dynamic_dim_idx] == 1) {
|
||||
// Single-token capture: every trailing dim is size 1 with the same stride, so
|
||||
// the match is ambiguous. The lowest index is the real axis; the rest are
|
||||
// ggml's size-1 padding. Bailing out here would bake the captured token count
|
||||
// into the static prefill model, which then runs with a different one.
|
||||
m_node_dynamic_dims[node] = first_matched_dim;
|
||||
} else if (matched_dim_count != 1) {
|
||||
m_node_dynamic_dims[node] = -1;
|
||||
GGML_LOG_WARN("ggml-openvino: cannot determine dynamic dim for CONT node '%s', src[0]: '%s'\n",
|
||||
node->name, node->src[0]->name);
|
||||
|
||||
@@ -47,6 +47,7 @@ struct ComputeParams {
|
||||
int seq_active_start = 0;
|
||||
int attention_size = -1;
|
||||
int attention_size_swa = -1;
|
||||
int attention_size_static = -1; // encoder/cross-attn KV fill level (whisper)
|
||||
int input_len = -1;
|
||||
int token_len_per_seq = -1;
|
||||
int past_kv_len = -1;
|
||||
@@ -84,14 +85,15 @@ struct ComputeParams {
|
||||
|
||||
struct RsWriteback {
|
||||
int slot_begin = 0; // first cache slot written by the CPY
|
||||
int src_begin = 0; // where the copied data starts in the source tensor (in rows of it)
|
||||
int src_begin = 0; // first source row or column copied by the CPY
|
||||
};
|
||||
|
||||
std::map<std::string, RsWriteback> rs_writebacks;
|
||||
// Offsets of the state cache writeback CPY nodes, keyed by node name. They change with the
|
||||
// batch (kv head, active sequence count, token count) and, with rollback enabled
|
||||
// (cparams.n_rs_seq > 0), the conv state is written back once per snapshot slot, each snapshot
|
||||
// taking a different conv_input window. Passed to the cached model as runtime inputs.
|
||||
// Destination slot offset of each state cache writeback CPY node, keyed by node name. It
|
||||
// changes with the batch (kv head, active sequence count) and, with rollback enabled
|
||||
// (cparams.n_rs_seq > 0), the conv state is written back once per snapshot slot. Passed to the
|
||||
// cached model as a runtime input. Dynamic models also receive the source-side offset; static
|
||||
// models use a fixed end-anchored offset in the translator.
|
||||
};
|
||||
|
||||
class GgmlOvDecoder : public ov::frontend::ggml::GgmlDecoder {
|
||||
|
||||
@@ -32,6 +32,8 @@ void ggml_openvino_device_config::init() {
|
||||
"GGML_OPENVINO_DEVICE",
|
||||
"GGML_OPENVINO_CACHE_DIR",
|
||||
"GGML_OPENVINO_DEBUG_NODE",
|
||||
"GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR",
|
||||
"GGML_OPENVINO_NPU_COMPILE_CONFIG",
|
||||
// Integer values (use ggml_openvino_getenv_int)
|
||||
"GGML_OPENVINO_PREFILL_CHUNK_SIZE",
|
||||
// Boolean toggles (treated as int flags via ggml_openvino_getenv_int)
|
||||
@@ -41,6 +43,9 @@ void ggml_openvino_device_config::init() {
|
||||
"GGML_OPENVINO_DUMP_IR",
|
||||
"GGML_OPENVINO_DEBUG_INPUT",
|
||||
"GGML_OPENVINO_DEBUG_OUTPUT",
|
||||
// Force the static (NPU-shape) compute path on any device, e.g. GGML_OPENVINO_DEVICE=CPU,
|
||||
// to test the static-shape translation without NPUW/real NPU hardware in the loop.
|
||||
"GGML_OPENVINO_FORCE_STATIC",
|
||||
"GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS",
|
||||
"GGML_OPENVINO_ENABLE_CACHE",
|
||||
"GGML_OPENVINO_DISABLE_CACHE",
|
||||
@@ -50,7 +55,7 @@ void ggml_openvino_device_config::init() {
|
||||
"GGML_OPENVINO_MEMORY_OPTIMIZE",
|
||||
"GGML_OPENVINO_RELEASE_WEIGHTS",
|
||||
"GGML_OPENVINO_REDUCE_COMPILE_MEM",
|
||||
"GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR",
|
||||
"GGML_OPENVINO_LOG_UNSUPPORTED_OPS",
|
||||
};
|
||||
|
||||
for (const char * const & env_var : env_var_names) {
|
||||
@@ -85,6 +90,11 @@ void ggml_openvino_device_config::init() {
|
||||
compile_config["NPUW_CACHE_DIR"] = cache_dir;
|
||||
compile_config.insert(ov::cache_mode(ov::CacheMode::OPTIMIZE_SIZE));
|
||||
}
|
||||
const char * compilation_mode_params =
|
||||
ggml_openvino_getenv_str("GGML_OPENVINO_NPU_COMPILE_CONFIG");
|
||||
if (compilation_mode_params && strlen(compilation_mode_params) > 0) {
|
||||
compile_config["NPU_COMPILATION_MODE_PARAMS"] = compilation_mode_params;
|
||||
}
|
||||
} else if (cache_dir && strlen(cache_dir) > 0) {
|
||||
compile_config.insert(ov::cache_dir(cache_dir));
|
||||
compile_config.insert(ov::cache_mode(ov::CacheMode::OPTIMIZE_SIZE));
|
||||
|
||||
@@ -908,11 +908,27 @@ static bool has_non_contiguous_view_input(const ggml_tensor * op) {
|
||||
}
|
||||
|
||||
static bool is_supported_flash_attn_pattern(const ggml_tensor * op) {
|
||||
// pattern of q,k,v should be q->op==PERMUTE, q->src[0]->op==VIEW, q->src[0]->src[0]->view_src==nullptr
|
||||
// Each Q/K/V input must follow one of:
|
||||
// PERMUTE -> VIEW -> base (view_src==nullptr) (llama KV-cache path)
|
||||
// PERMUTE -> RESHAPE -> base (view_src==nullptr) (whisper Q)
|
||||
// VIEW -> base (view_src==nullptr) (whisper K/V from kv_pad)
|
||||
for (int i = 0; i < 3; i++) {
|
||||
const ggml_tensor * src = op->src[i];
|
||||
if (src->op != GGML_OP_PERMUTE || src->src[0] == nullptr || src->src[0]->op != GGML_OP_VIEW ||
|
||||
src->src[0]->src[0] == nullptr || src->src[0]->src[0]->view_src != nullptr) {
|
||||
if (src->op == GGML_OP_PERMUTE) {
|
||||
if (src->src[0] == nullptr) {
|
||||
return false;
|
||||
}
|
||||
if (src->src[0]->op != GGML_OP_VIEW && src->src[0]->op != GGML_OP_RESHAPE) {
|
||||
return false;
|
||||
}
|
||||
if (src->src[0]->src[0] == nullptr || src->src[0]->src[0]->view_src != nullptr) {
|
||||
return false;
|
||||
}
|
||||
} else if (src->op == GGML_OP_VIEW) {
|
||||
if (src->src[0] == nullptr || src->src[0]->view_src != nullptr) {
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
@@ -1030,18 +1046,29 @@ static bool is_msa_block_mask_expansion(const ggml_tensor * op) {
|
||||
return tensor_name_starts_with(src, "msa_block_mask");
|
||||
}
|
||||
|
||||
static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
namespace {
|
||||
struct ggml_openvino_op_support {
|
||||
bool is_supported = true;
|
||||
std::string reason;
|
||||
|
||||
operator bool() const {
|
||||
return is_supported;
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
static ggml_openvino_op_support is_op_supported_case(const ggml_tensor * op) {
|
||||
if (is_msa_block_mask_expansion(op)) {
|
||||
return true;
|
||||
return {false, "MSA block mask expansion is not supported"};
|
||||
}
|
||||
|
||||
switch (op->op) {
|
||||
case GGML_OP_CONCAT: {
|
||||
if (op->type == GGML_TYPE_I64) {
|
||||
return true;
|
||||
return {false, "CONCAT with I64 type is not supported"};
|
||||
}
|
||||
if (ggml_openvino_get_device_name() == "GPU" && op->type == GGML_TYPE_BF16 && has_view_op_input(op)) {
|
||||
return true;
|
||||
return {false, "CONCAT with BF16 type and VIEW input is not supported on GPU"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1052,24 +1079,21 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
|
||||
// OpenVINO SET translation currently supports dst layouts that match src0 strides.
|
||||
if (op->src[0] == nullptr || nb1 != op->src[0]->nb[1] || nb2 != op->src[0]->nb[2] || nb3 != op->src[0]->nb[3]) {
|
||||
// std::cout << "Unsupported SET op with dst nb1=" << nb1 << ", nb2=" << nb2 << ", nb3=" << nb3
|
||||
// << " that does not match src0 strides nb[1]="
|
||||
// << (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[1]) : "null")
|
||||
// << ", nb[2]=" << (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[2]) : "null")
|
||||
// << ", nb[3]=" << (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[3]) : "null")
|
||||
// << std::endl;
|
||||
return true;
|
||||
return {false, "SET op with dst nb1=" + std::to_string(nb1) + ", nb2=" + std::to_string(nb2) + ", nb3=" + std::to_string(nb3) +
|
||||
" that does not match src0 strides nb[1]=" + (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[1]) : "null") +
|
||||
", nb[2]=" + (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[2]) : "null") +
|
||||
", nb[3]=" + (op->src[0] != nullptr ? std::to_string(op->src[0]->nb[3]) : "null")};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_GET_ROWS:
|
||||
case GGML_OP_SET_ROWS: {
|
||||
if (op->ne[3] != 1) {
|
||||
return true;
|
||||
return {false, "GET_ROWS/SET_ROWS with ne[3] != 1 (ne[3]=" + std::to_string(op->ne[3]) + ") is not supported"};
|
||||
}
|
||||
if (op->op == GGML_OP_GET_ROWS && ggml_openvino_get_device_name() == "GPU" &&
|
||||
op->src[0]->type == GGML_TYPE_BF16) {
|
||||
return true;
|
||||
return {false, "GET_ROWS with BF16 src0 is not supported on GPU"};
|
||||
}
|
||||
if (op->ne[0] == 256 && (op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K ||
|
||||
op->src[0]->type == GGML_TYPE_Q4_1 || op->src[0]->type == GGML_TYPE_Q5_1)) {
|
||||
@@ -1078,14 +1102,14 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// make_int8_weights/make_int4_weights: dequant is done in f16, not f32, to keep the
|
||||
// Convert/Subtract/Multiply chain fusable into GatherMatmulCompressed/FullyConnectedCompressed
|
||||
// for the shared non-test code paths).
|
||||
return true;
|
||||
return {false, "GET_ROWS/SET_ROWS with ne[0] == 256 and type " + std::string(ggml_type_name(op->src[0]->type)) +
|
||||
" rejected due to f16-arithmetic dequant rounding errors that intermittently exceed 1e-7 NMSE threshold"};
|
||||
}
|
||||
|
||||
break;
|
||||
}
|
||||
case GGML_OP_RESHAPE: {
|
||||
if (strncmp(op->name, "ffn_norm_exps", sizeof("ffn_norm_exps") - 1) == 0) {
|
||||
return true;
|
||||
return {false, "RESHAPE for ffn_norm_exps is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1093,11 +1117,13 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
case GGML_OP_MUL:
|
||||
case GGML_OP_SUB: {
|
||||
if (op->src[1]->op == GGML_OP_PERMUTE) {
|
||||
return true;
|
||||
return {false, "ADD/MUL/SUB with PERMUTE src1 is not supported"};
|
||||
}
|
||||
for (int i = 0; i < 4; i++) {
|
||||
if (op->src[0]->ne[i] != op->src[1]->ne[i] && (op->src[0]->ne[i] != 1 && op->src[1]->ne[i] != 1)) {
|
||||
return true;
|
||||
return {false, "ADD/MUL/SUB with incompatible broadcast shapes: src0->ne[" + std::to_string(i) + "]=" +
|
||||
std::to_string(op->src[0]->ne[i]) + ", src1->ne[" + std::to_string(i) + "]=" +
|
||||
std::to_string(op->src[1]->ne[i])};
|
||||
}
|
||||
}
|
||||
break;
|
||||
@@ -1106,7 +1132,7 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// Keep support aligned with the CPU backend implementation, which only handles f32 inputs/output and i32 ids.
|
||||
if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type != GGML_TYPE_F32 ||
|
||||
op->src[2]->type != GGML_TYPE_I32) {
|
||||
return true;
|
||||
return {false, "ADD_ID only supports F32 inputs/output and I32 ids"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1116,14 +1142,27 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// until the fused GPU kernel is reliable. (falied case llama-arch-test mpt)
|
||||
if (ggml_openvino_get_device_name() == "GPU" && op->src[1]->ne[0] == op->ne[0] &&
|
||||
op->src[1]->ne[1] == 1 && op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1) {
|
||||
return true;
|
||||
return {false, "DIV per-channel scale broadcast is not supported on GPU"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_POOL_2D: {
|
||||
const auto& name = ggml_openvino_get_device_name();
|
||||
if (name == "GPU") {
|
||||
const int32_t * params = op->op_params;
|
||||
const int k0 = params[1];
|
||||
const int k1 = params[2];
|
||||
const int p0 = params[5];
|
||||
const int p1 = params[6];
|
||||
if ((p0 > 0 || p1 > 0) && (k0 < 3 || k1 < 3)) {
|
||||
return {false, "POOL_2D with padding and kernel size < 3 is not supported on " + name};
|
||||
}
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_SUM_ROWS: {
|
||||
// if the input is PERMUTE skip
|
||||
if (op->src[0]->op == GGML_OP_PERMUTE) {
|
||||
return true;
|
||||
return {false, "SUM_ROWS with PERMUTE input is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1140,54 +1179,51 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// accuracy drift in the OpenVINO path. Restrict by scale=1.0 to avoid
|
||||
// affecting non-gemma3n models such as Llama-3.2.
|
||||
if (fabsf(scale - 1.0f) < 1e-6f && is_gemma3n_flash_attn_pattern(op)) {
|
||||
return true;
|
||||
return {false, "FLASH_ATTN_EXT gemma3n pattern on GPU is not supported"};
|
||||
}
|
||||
|
||||
if (op->src[4] != nullptr) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n");
|
||||
return true;
|
||||
return {false, "FLASH_ATTN_EXT with sinks is not supported"};
|
||||
}
|
||||
if (!is_supported_flash_attn_pattern(op)) {
|
||||
return true;
|
||||
return {false, "FLASH_ATTN_EXT unsupported attention pattern"};
|
||||
}
|
||||
if (max_bias > 0) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with max_bias > 0\n");
|
||||
return true;
|
||||
return {false, "FLASH_ATTN_EXT with max_bias > 0 (max_bias=" + std::to_string(max_bias) + ") is not supported"};
|
||||
}
|
||||
if (logit_softcap != 0) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with logit_softcap != 0\n");
|
||||
return true;
|
||||
return {false, "FLASH_ATTN_EXT with logit_softcap != 0 (logit_softcap=" + std::to_string(logit_softcap) + ") is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_PERMUTE: {
|
||||
if (op->type == GGML_TYPE_BF16) {
|
||||
// err msg: [GPU] Could not find a suitable kernel for transpose
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support PERMUTE with BF16 type\n");
|
||||
return true;
|
||||
if (op->type == GGML_TYPE_BF16 && ggml_openvino_get_device_name() == "GPU") {
|
||||
return {false, "PERMUTE with BF16 type is not supported on GPU"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_CPY: {
|
||||
if (op->src[0]->type == GGML_TYPE_BF16 || op->src[1]->type == GGML_TYPE_BF16) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support CPY with non-contiguous data or bf16 types\n");
|
||||
return true;
|
||||
return {false, "CPY with BF16 src type is not supported"};
|
||||
}
|
||||
// CPY to a quantized destination (e.g. f32 -> q4_0) is numerically unstable with OpenVINO backend.
|
||||
if (ggml_is_quantized(op->type)) {
|
||||
return true;
|
||||
return {false, "CPY to quantized destination (e.g. f32 -> q4_0) is numerically unstable"};
|
||||
}
|
||||
if (ggml_nelements(op->src[0]) != ggml_nelements(op->src[1])) {
|
||||
return true;
|
||||
return {false, "CPY with mismatched element counts is not supported: src0=" + std::to_string(ggml_nelements(op->src[0])) +
|
||||
" != src1=" + std::to_string(ggml_nelements(op->src[1]))};
|
||||
}
|
||||
// op test case with non-contiguous src or dst
|
||||
if ((op->ne[0] == 3 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2) ||
|
||||
(op->ne[0] == 1 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2) ||
|
||||
(op->ne[0] == 2 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2)) {
|
||||
return true;
|
||||
return {false, "CPY with non-contiguous shape [" + std::to_string(op->ne[0]) + ", " +
|
||||
std::to_string(op->ne[1]) + ", " + std::to_string(op->ne[2]) + ", " +
|
||||
std::to_string(op->ne[3]) + "] is not supported"};
|
||||
}
|
||||
if (!cpy_output_view_is_supported(op)) {
|
||||
return true;
|
||||
return {false, "CPY with non-contiguous output view is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1196,13 +1232,14 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
ggml_is_quantized(op->src[0]->type) && strcmp(op->src[0]->name, "a") == 0 &&
|
||||
strcmp(op->src[1]->name, "b") == 0 && op->src[0]->ne[1] == 1 && op->src[1]->ne[1] == 64 &&
|
||||
op->src[0]->ne[0] == 256 && op->src[1]->ne[0] == 256) {
|
||||
return true;
|
||||
return {false, "MUL_MAT quantized benchmark test case on GPU is not supported"};
|
||||
}
|
||||
if (op->src[0]->ne[3] != op->src[1]->ne[3] && op->src[0]->ne[3] != 1 && op->src[1]->ne[3] != 1) {
|
||||
return true;
|
||||
return {false, "MUL_MAT with incompatible broadcast on ne[3]: src0->ne[3]=" + std::to_string(op->src[0]->ne[3]) +
|
||||
", src1->ne[3]=" + std::to_string(op->src[1]->ne[3])};
|
||||
}
|
||||
if (op->src[0]->op == GGML_OP_VIEW && op->src[1]->op == GGML_OP_VIEW) {
|
||||
return true;
|
||||
return {false, "MUL_MAT with both inputs as VIEW is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1210,16 +1247,17 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// Single-expert (or empty) MUL_MAT_ID is a degenerate shape that stresses GatherMatmul edge
|
||||
// cases and never occurs in real MoE; let it fall back to CPU.
|
||||
if (op->src[0] != nullptr && op->src[0]->ne[2] <= 1) {
|
||||
return true;
|
||||
return {false, "MUL_MAT_ID with single-expert or empty ne[2] <= 1 (ne[2]=" +
|
||||
std::to_string(op->src[0]->ne[2]) + ") is not supported"};
|
||||
}
|
||||
if (ggml_openvino_get_device_name() == "GPU" && op->src[0] != nullptr && op->src[0]->type == GGML_TYPE_BF16) {
|
||||
return true;
|
||||
return {false, "MUL_MAT_ID with BF16 weights on GPU is not supported"};
|
||||
}
|
||||
// GPU MUL_MAT_ID uses a Gather+MatMul fallback because the GPU plugin rejects internal
|
||||
// GatherMatmul for these test shapes. Skip cases that would materialize a large selected
|
||||
// expert-weight temporary.
|
||||
if (ggml_openvino_get_device_name() == "GPU" && mul_mat_id_requires_large_tmp(op)) {
|
||||
return true;
|
||||
return {false, "MUL_MAT_ID requires large temporary on GPU"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1229,51 +1267,46 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
const int mode = op_params[2];
|
||||
if (op_params[15] != 0) {
|
||||
// FIXME: support ggml_rope_set_offset
|
||||
return true;
|
||||
return {false, "ggml_rope_set_offset is not supported"};
|
||||
}
|
||||
if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_IMROPE) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with mode %d\n", mode);
|
||||
return true;
|
||||
return {false, "ROPE with mode " + std::to_string(mode) + " is not supported"};
|
||||
}
|
||||
const int64_t head_dim = op->src[0]->ne[0];
|
||||
const int64_t rope_dims = n_dims == 0 ? head_dim : n_dims;
|
||||
if (rope_dims <= 0 || rope_dims > head_dim || (rope_dims % 2) != 0) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with n_dims %d and src[0]->ne[0] %ld\n", n_dims,
|
||||
// op->src[0]->ne[0]);
|
||||
return true;
|
||||
return {false, "ROPE with n_dims=" + std::to_string(n_dims) + ", head_dim=" + std::to_string(head_dim) + " is not supported"};
|
||||
}
|
||||
if (op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type));
|
||||
return true;
|
||||
return {false, "ROPE with type " + std::string(ggml_type_name(op->type)) + " is not supported"};
|
||||
}
|
||||
if (op->src[0]->op == GGML_OP_VIEW) {
|
||||
if (op->src[0]->view_src->ne[1] != op->src[0]->ne[2]) {
|
||||
// GGML_LOG_WARN(
|
||||
// "OpenVINO backend does not support ROPE with src[0]->view_src->ne[1] %ld != src[0]->ne[2] "
|
||||
// "%ld\n",
|
||||
// op->src[0]->view_src->ne[1], op->src[0]->ne[2]);
|
||||
return true;
|
||||
const struct ggml_tensor * view = op->src[0];
|
||||
const struct ggml_tensor * view_src = view->view_src;
|
||||
if (view_src->ne[1] != view->ne[1] || view_src->ne[2] != view->ne[2] || view_src->ne[3] != view->ne[3]) {
|
||||
return {false, "ROPE with view_src->ne [" + std::to_string(view_src->ne[1]) + ", " +
|
||||
std::to_string(view_src->ne[2]) + ", " + std::to_string(view_src->ne[3]) +
|
||||
"] != view->ne [" + std::to_string(view->ne[1]) + ", " +
|
||||
std::to_string(view->ne[2]) + ", " + std::to_string(view->ne[3]) +
|
||||
"] is not supported"};
|
||||
}
|
||||
}
|
||||
if (mode == GGML_ROPE_TYPE_IMROPE &&
|
||||
(op->src[2] != 0 || ((const float *) op_params)[6] != 1 || ((const float *) op_params)[7] != 0 ||
|
||||
((const float *) op_params)[8] != 1)) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support IMROPE with freq_factors, freq_scale, ext_factor, and attn_factor\n");
|
||||
return true;
|
||||
return {false, "IMROPE with freq_factors, freq_scale, ext_factor, and attn_factor is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_TRANSPOSE: {
|
||||
// if the type is bf16, will return true
|
||||
if (op->type == GGML_TYPE_BF16) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support CONT with BF16 type\n");
|
||||
return true;
|
||||
return {false, "TRANSPOSE with BF16 type is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_REPEAT: {
|
||||
if (ggml_openvino_get_device_name() == "GPU" && op->type == GGML_TYPE_BF16) {
|
||||
return true;
|
||||
return {false, "REPEAT with BF16 type is not supported on GPU"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1285,15 +1318,15 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// return true;
|
||||
// }
|
||||
if (op->src[2]->op == GGML_OP_PERMUTE) {
|
||||
return true;
|
||||
return {false, "GATED_DELTA_NET with PERMUTE src2 is not supported"};
|
||||
}
|
||||
// kda (per-key-dimension gating) not supported by fused GatedDeltaNet op
|
||||
if (op->src[3]->ne[0] != 1) {
|
||||
return true;
|
||||
return {false, "GATED_DELTA_NET with kda (per-key-dimension gating) is not supported"};
|
||||
}
|
||||
// K > 1 (multiple state snapshots) not supported by fused op
|
||||
if (((const int32_t *) op->op_params)[0] > 1) {
|
||||
return true;
|
||||
return {false, "GATED_DELTA_NET with K > 1 (multiple state snapshots) is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
@@ -1307,17 +1340,17 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
|
||||
// Skip TOPK_MOE fused tests until it is fully supported.
|
||||
// The argsort_top_k VIEW wrapping ARGSORT is named "selected_experts" in test_topk_moe.
|
||||
if (strcmp(op->name, "selected_experts") == 0) {
|
||||
return true;
|
||||
return {false, "VIEW for selected_experts (argsort_top_k) is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return false;
|
||||
return {true, ""};
|
||||
}
|
||||
|
||||
static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
|
||||
static ggml_openvino_op_support ggml_backend_openvino_device_supports_op_impl(ggml_backend_dev_t dev, const ggml_tensor * op) {
|
||||
GGML_ASSERT(dev->reg != nullptr);
|
||||
|
||||
static std::unordered_set<ggml_type> supported_types{
|
||||
@@ -1367,48 +1400,41 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
|
||||
case GGML_OP_UNARY: {
|
||||
auto supported = supported_unary_ops.find(ggml_get_unary_op(op)) != supported_unary_ops.end();
|
||||
if (!supported) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support unary op %s\n", ggml_unary_op_name(ggml_get_unary_op(op)));
|
||||
return false;
|
||||
return {false, "unary op " + std::string(ggml_unary_op_name(ggml_get_unary_op(op))) + " has no op translator"};
|
||||
}
|
||||
if (ggml_get_unary_op(op) == GGML_UNARY_OP_EXP && op->type == GGML_TYPE_F32) {
|
||||
return false;
|
||||
return {false, "UNARY_EXP with F32 type is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
case GGML_OP_GLU: {
|
||||
auto supported = supported_glu_ops.find(ggml_get_glu_op(op)) != supported_glu_ops.end();
|
||||
if (!supported) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support GLU op %s\n", ggml_glu_op_name(ggml_get_glu_op(op)));
|
||||
return false;
|
||||
return {false, "GLU op " + std::string(ggml_glu_op_name(ggml_get_glu_op(op))) + " has no op translator"};
|
||||
}
|
||||
// if (has_view_op_input(op)) {
|
||||
// // GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
|
||||
// // ggml_glu_op_name(ggml_get_glu_op(op)));
|
||||
// return false;
|
||||
// return {false, "GLU op " + std::string(ggml_glu_op_name(ggml_get_glu_op(op))) + " with view input is not supported"};
|
||||
// }
|
||||
if (op->src[1] == nullptr && op->src[0]->ne[0] % 2 != 0) {
|
||||
// triggers bug in ov gpu
|
||||
return false;
|
||||
return {false, "GLU op with odd src0 ne[0] and null src1 is not supported"};
|
||||
}
|
||||
break;
|
||||
}
|
||||
default: {
|
||||
auto supported = supported_ops.find(op->op) != supported_ops.end();
|
||||
if (!supported) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support op %s\n", ggml_op_name(op->op));
|
||||
return false;
|
||||
return {false, "op " + std::string(ggml_op_name(op->op)) + " has no op translator"};
|
||||
}
|
||||
static std::set<ggml_op> ops_not_support_view_input{};
|
||||
if (ops_not_support_view_input.find(op->op) != ops_not_support_view_input.end() && has_view_op_input(op)) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support op %s with view input\n", ggml_op_name(op->op));
|
||||
return false;
|
||||
return {false, "op " + std::string(ggml_op_name(op->op)) + " with VIEW input is not supported"};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (supported_types.find(op->type) == supported_types.end()) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(op->type));
|
||||
return false;
|
||||
return {false, "tensor type " + std::string(ggml_type_name(op->type)) + " is not supported"};
|
||||
}
|
||||
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
||||
auto * src = op->src[i];
|
||||
@@ -1416,21 +1442,32 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
|
||||
break;
|
||||
}
|
||||
if (supported_types.find(src->type) == supported_types.end()) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(src->type));
|
||||
return false;
|
||||
return {false, "src[" + std::to_string(i) + "] type " + std::string(ggml_type_name(src->type)) + " is not supported"};
|
||||
}
|
||||
const bool is_supported_3d_moe_expert =
|
||||
op->op == GGML_OP_MUL_MAT_ID && i == 0 && (src->type == GGML_TYPE_MXFP4 || src->ne[3] == 1);
|
||||
if (ggml_is_quantized(src->type) && src->ne[2] != 1 && !is_supported_3d_moe_expert) {
|
||||
// GGML_LOG_WARN("OpenVINO backend does not support 3D quantized tensors\n");
|
||||
return false;
|
||||
return {false, "3D quantized tensor for src[" + std::to_string(i) + "] is not supported"};
|
||||
}
|
||||
}
|
||||
|
||||
if (is_op_unsupported_case(op)) {
|
||||
return false;
|
||||
auto op_support_case = is_op_supported_case(op);
|
||||
if (!op_support_case.is_supported) {
|
||||
return op_support_case;
|
||||
}
|
||||
return true;
|
||||
return {true, ""};
|
||||
}
|
||||
|
||||
static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
|
||||
auto res = ggml_backend_openvino_device_supports_op_impl(dev, op);
|
||||
if (!res.is_supported) {
|
||||
static const bool log_unsupported = ggml_openvino_getenv_int("GGML_OPENVINO_LOG_UNSUPPORTED_OPS") != 0;
|
||||
if (log_unsupported) {
|
||||
GGML_LOG_WARN("OpenVINO op unsupported: op '%s' (%s), type %s: %s\n",
|
||||
op->name, ggml_op_name(op->op), ggml_type_name(op->type), res.reason.c_str());
|
||||
}
|
||||
}
|
||||
return res.is_supported;
|
||||
}
|
||||
|
||||
static bool ggml_backend_openvino_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
|
||||
|
||||
@@ -3,8 +3,11 @@
|
||||
#include "../utils.h"
|
||||
|
||||
#include <climits>
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
#include <numeric>
|
||||
#include <openvino/frontend/exception.hpp>
|
||||
#include <openvino/op/add.hpp>
|
||||
#include <openvino/op/concat.hpp>
|
||||
#include <openvino/op/constant.hpp>
|
||||
@@ -12,9 +15,14 @@
|
||||
#include <openvino/op/gather.hpp>
|
||||
#include <openvino/op/multiply.hpp>
|
||||
#include <openvino/op/negative.hpp>
|
||||
#include <openvino/op/range.hpp>
|
||||
#include <openvino/op/reshape.hpp>
|
||||
#include <openvino/op/scatter_update.hpp>
|
||||
#include <openvino/op/shape_of.hpp>
|
||||
#include <openvino/op/slice.hpp>
|
||||
#include <openvino/op/squeeze.hpp>
|
||||
#include <openvino/op/subtract.hpp>
|
||||
#include <vector>
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
@@ -61,10 +69,27 @@ OutputVector translate_cpy(const NodeContext & context) {
|
||||
return rename_outputs_with_suffix({res}, context.get_name());
|
||||
}
|
||||
|
||||
// Recurrent state cache writeback into a slot block of the cache. Where the block starts and
|
||||
// where the copied data starts in the source are runtime inputs, so the cached model works for
|
||||
// any kv head, active sequence count and token count. The result is the full updated cache.
|
||||
// Recurrent state cache writeback into a slot block of the cache. Where the block starts is a
|
||||
// runtime input, so the cached model works for any kv head and active sequence count. The
|
||||
// result is the full updated cache.
|
||||
// op_case 1: gated-delta-net state, op_case 2: conv state, op_case 3: defrag remainder.
|
||||
if (op_case == 3) {
|
||||
// With -np 1 (and generally whenever there is no defrag remainder) this GET_ROWS gathers
|
||||
// zero rows: nothing to write back, and the cache is unchanged. NPU rejects zero-size
|
||||
// tensors, so short-circuit instead of building a degenerate Slice/Concat chain.
|
||||
bool is_empty = false;
|
||||
if (input_shape.rank().is_static()) {
|
||||
for (const auto & d : input_shape) {
|
||||
if (d.is_static() && d.get_length() == 0) {
|
||||
is_empty = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (is_empty) {
|
||||
return {context.get_input(1)};
|
||||
}
|
||||
}
|
||||
const std::string slot_begin_name = "rs_slot_begin_" + context.get_name();
|
||||
const bool slice_assign =
|
||||
context.has_input(slot_begin_name) && !context.is_stateful() && (op_case >= 1 && op_case <= 3);
|
||||
@@ -81,19 +106,49 @@ OutputVector translate_cpy(const NodeContext & context) {
|
||||
ov::Output<ov::Node> begin = context.get_input(slot_begin_name);
|
||||
auto base = context.get_input(1);
|
||||
if (op_case == 1) {
|
||||
// GDN packs [attn | state snapshots]; the state part runs from src_begin to the end.
|
||||
auto src_begin = context.get_input("rs_src_begin_" + context.get_name());
|
||||
auto state_part = std::make_shared<ov::op::v8::Slice>(context.get_input(0), src_begin, int_max, one, axis);
|
||||
ov::Output<ov::Node> state_begin;
|
||||
const std::string src_begin_name = "rs_src_begin_" + context.get_name();
|
||||
if (context.has_input(src_begin_name)) {
|
||||
state_begin = context.get_input(src_begin_name);
|
||||
} else {
|
||||
auto ssm_state_size = context.get_ssm_state_size();
|
||||
if (context.has_input("s_copy_active_slot_len")) {
|
||||
auto len = context.get_input("s_copy_active_slot_len");
|
||||
auto state_rows = std::make_shared<ov::op::v1::Multiply>(
|
||||
ov::op::v0::Constant::create(ov::element::i64, {1}, {ssm_state_size}), len);
|
||||
state_begin = std::make_shared<ov::op::v0::Negative>(state_rows);
|
||||
} else {
|
||||
state_begin = ov::op::v0::Constant::create(ov::element::i64, {1}, {-ssm_state_size});
|
||||
}
|
||||
}
|
||||
auto state_part =
|
||||
std::make_shared<ov::op::v8::Slice>(context.get_input(0), state_begin, int_max, one, axis);
|
||||
src = std::make_shared<ov::op::v1::Reshape>(state_part, feature, false);
|
||||
} else if (op_case == 2) {
|
||||
// conv_input is [previous conv state | new tokens]; copy the conv_kernel_size - 1 wide
|
||||
// window starting at src_begin, which is the snapshot this writeback corresponds to.
|
||||
// conv_input is [previous conv state | new tokens]; the snapshot is the conv_kernel_size - 1
|
||||
// columns ending at the last *valid* token. Gather (rather than Slice) keeps the output
|
||||
// shape static even though the window start is a runtime value.
|
||||
auto window_size = (int64_t) input_shape[3].get_length();
|
||||
auto src_begin = context.get_input("rs_src_begin_" + context.get_name());
|
||||
auto src_end = std::make_shared<ov::op::v1::Add>(
|
||||
src_begin, ov::op::v0::Constant::create(ov::element::i64, {1}, {window_size}));
|
||||
auto window = std::make_shared<ov::op::v8::Slice>(context.get_input(0), src_begin, src_end, one,
|
||||
ov::op::v0::Constant::create(ov::element::i64, {1}, {3}));
|
||||
ov::Output<ov::Node> window;
|
||||
auto col_axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
|
||||
const std::string src_begin_name = "rs_src_begin_" + context.get_name();
|
||||
if (context.has_input(src_begin_name)) {
|
||||
auto src_begin = context.get_input(src_begin_name);
|
||||
auto src_end = std::make_shared<ov::op::v1::Add>(
|
||||
src_begin, ov::op::v0::Constant::create(ov::element::i64, {1}, {window_size}));
|
||||
window = std::make_shared<ov::op::v8::Slice>(context.get_input(0), src_begin, src_end, one, col_axis);
|
||||
} else if (context.has_input("chunk_valid_len")) {
|
||||
std::vector<int64_t> offsets(window_size);
|
||||
std::iota(offsets.begin(), offsets.end(), 0);
|
||||
auto indices = std::make_shared<ov::op::v1::Add>(
|
||||
ov::op::v0::Constant::create(ov::element::i64, {(size_t) window_size}, offsets),
|
||||
context.get_input("chunk_valid_len"));
|
||||
window = std::make_shared<ov::op::v8::Gather>(context.get_input(0), indices, col_axis);
|
||||
} else {
|
||||
auto window_begin = ov::op::v0::Constant::create(ov::element::i64, {1}, {-window_size});
|
||||
window =
|
||||
std::make_shared<ov::op::v8::Slice>(context.get_input(0), window_begin, int_max, one, col_axis);
|
||||
}
|
||||
const auto base_shape = base.get_partial_shape();
|
||||
FRONT_END_OP_CONVERSION_CHECK(base_shape.rank().is_static() && base_shape.rank().get_length() == 4,
|
||||
"CPY conv state cache update requires rank-4 base cache");
|
||||
@@ -157,6 +212,63 @@ OutputVector translate_cpy(const NodeContext & context) {
|
||||
|
||||
auto input = process_view_input_new(context, 0);
|
||||
|
||||
if (op_case == 5 || op_case == 6) {
|
||||
auto input_shape = context.get_input_shape(0);
|
||||
auto output_shape = context.get_output_shape();
|
||||
auto dst_ggml_shape = context.get_view_input_ggml_shape(1, 0);
|
||||
auto dst_stride = context.get_view_input_stride(1, 0);
|
||||
size_t offset_bytes = context.get_view_input_offset(1, 0);
|
||||
auto n_state = (int64_t) context.get_input_shape(0)[3].get_length();
|
||||
auto n_state_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {n_state});
|
||||
auto kv_buf = context.get_input(1); // shape {1,1,1,N}
|
||||
|
||||
Output<Node> token_len_per_seq;
|
||||
Output<Node> n_write_dyn;
|
||||
if (context.has_input("token_len_per_seq")) {
|
||||
token_len_per_seq = context.get_input("token_len_per_seq");
|
||||
n_write_dyn = std::make_shared<ov::op::v1::Multiply>(token_len_per_seq, n_state_c);
|
||||
} else {
|
||||
n_write_dyn = ov::op::v0::Constant::create(ov::element::i64, {1}, {(int64_t) dst_ggml_shape[3]});
|
||||
}
|
||||
size_t elem_size = dst_stride[3];
|
||||
FRONT_END_OP_CONVERSION_CHECK(elem_size > 0, "CPY KV cache view update has invalid element size");
|
||||
int64_t start_elem = (int64_t) (offset_bytes / elem_size);
|
||||
// op_case 5: decoder self-attention – write offset advances each step.
|
||||
// op_case 6: encoder self-attn or cross-attn – offset fixed at compile time.
|
||||
const bool is_decoder_self_attn = (op_case == 5);
|
||||
auto ones_c = ov::op::v0::Constant::create(ov::element::i64, {3}, std::vector<int64_t>{1, 1, 1});
|
||||
auto new_shape = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{ones_c, n_write_dyn}, 0);
|
||||
|
||||
auto reshaped = std::make_shared<ov::op::v1::Reshape>(input, new_shape, false);
|
||||
auto data = std::make_shared<ov::op::v0::Convert>(reshaped, context.get_output_type());
|
||||
// Indices [start_elem .. start_elem + n_write) on axis 3 of {1,1,1,N}
|
||||
// For decoder self-attention the write offset advances each step, so compute it
|
||||
// dynamically from the model inputs: start = (attention_size - token_len_per_seq) * n_state.
|
||||
// For encoder self-attn and cross-attn the offset is fixed at graph-compile time.
|
||||
ov::Output<ov::Node> start;
|
||||
if (is_decoder_self_attn && context.has_input("attention_size") && context.has_input("token_len_per_seq")) {
|
||||
auto attention_size_in = context.get_input("attention_size");
|
||||
auto token_len_in = context.get_input("token_len_per_seq");
|
||||
auto past_tokens = std::make_shared<ov::op::v1::Subtract>(attention_size_in, token_len_in);
|
||||
auto new_start = std::make_shared<ov::op::v1::Multiply>(past_tokens, n_state_c);
|
||||
start = std::make_shared<ov::op::v1::Add>(
|
||||
new_start, ov::op::v0::Constant::create(ov::element::i64, {1}, {start_elem}));
|
||||
} else {
|
||||
start = ov::op::v0::Constant::create(ov::element::i64, {1}, {start_elem});
|
||||
}
|
||||
auto start_squeezed = std::make_shared<ov::op::v0::Squeeze>(start);
|
||||
auto end = std::make_shared<ov::op::v1::Add>(start_squeezed, n_write_dyn);
|
||||
auto end_squeezed = std::make_shared<ov::op::v0::Squeeze>(end);
|
||||
auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
|
||||
auto step_squeezed = std::make_shared<ov::op::v0::Squeeze>(step);
|
||||
auto indices =
|
||||
std::make_shared<ov::op::v4::Range>(start_squeezed, end_squeezed, step_squeezed, ov::element::i64);
|
||||
auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
|
||||
|
||||
auto kv_updated = std::make_shared<ov::op::v3::ScatterUpdate>(kv_buf, indices, data, axis);
|
||||
return rename_outputs_with_suffix({kv_updated}, context.get_name());
|
||||
}
|
||||
|
||||
if (input_shape != output_shape) {
|
||||
auto new_shape = ov::op::v0::Constant::create(
|
||||
ov::element::i64, {static_cast<size_t>(output_shape.rank().get_length())}, output_shape.to_shape());
|
||||
|
||||
@@ -3,8 +3,8 @@
|
||||
#include "../utils.h"
|
||||
#include "ggml-openvino/ggml-openvino-extra.h"
|
||||
|
||||
#include <cstddef>
|
||||
#include <cstdint>
|
||||
#include <cstdlib>
|
||||
#include <memory>
|
||||
#include <openvino/op/add.hpp>
|
||||
#include <openvino/op/broadcast.hpp>
|
||||
@@ -15,6 +15,7 @@
|
||||
#include <openvino/op/multiply.hpp>
|
||||
#include <openvino/op/reshape.hpp>
|
||||
#include <openvino/op/scaled_dot_product_attention.hpp>
|
||||
#include <openvino/op/slice.hpp>
|
||||
#include <openvino/op/softmax.hpp>
|
||||
#include <openvino/op/transpose.hpp>
|
||||
#include <openvino/op/unsqueeze.hpp>
|
||||
@@ -24,13 +25,62 @@ namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace op {
|
||||
static ov::Output<ov::Node> reshape_flat_kv(const ov::Output<ov::Node> & kv_flat,
|
||||
size_t view_offset_bytes,
|
||||
size_t nb1_bytes,
|
||||
int64_t n_head,
|
||||
int64_t head_size,
|
||||
const ov::Output<ov::Node> & attention_size) {
|
||||
int64_t n_state = n_head * head_size;
|
||||
int64_t layer_start_elem = (int64_t) (view_offset_bytes / (nb1_bytes / n_state));
|
||||
// Dynamic slice: [layer_start_elem, layer_start_elem + n_kv * n_state)
|
||||
auto start_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {layer_start_elem});
|
||||
auto n_state_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {n_state});
|
||||
// end = start + attention_size * n_state (both static + dynamic)
|
||||
auto kv_len_elems = std::make_shared<ov::op::v1::Multiply>(attention_size, n_state_c);
|
||||
auto end_c = std::make_shared<ov::op::v1::Add>(start_c, kv_len_elems);
|
||||
auto step_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
|
||||
auto axis_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
|
||||
auto sliced = std::make_shared<ov::op::v8::Slice>(kv_flat, start_c, end_c, step_c, axis_c);
|
||||
|
||||
// KV cache is laid out as {n_kv, n_head, head_size} in memory
|
||||
// Reshape to {1, n_kv, n_head, head_size}, then transpose to {1, n_head, n_kv, head_size}
|
||||
// as required by SDPA.
|
||||
auto one_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
|
||||
auto n_head_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {n_head});
|
||||
auto head_size_c = ov::op::v0::Constant::create(ov::element::i64, {1}, {head_size});
|
||||
// reshape: {n_kv*n_state} -> {1, n_kv, n_head, head_size}
|
||||
auto new_shape =
|
||||
std::make_shared<ov::op::v0::Concat>(ov::OutputVector{one_c, attention_size, n_head_c, head_size_c}, 0);
|
||||
auto reshaped = std::make_shared<ov::op::v1::Reshape>(sliced, new_shape, false);
|
||||
// transpose: {1, n_kv, n_head, head_size} -> {1, n_head, n_kv, head_size}
|
||||
auto perm = ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3});
|
||||
auto ret = std::make_shared<ov::op::v1::Transpose>(reshaped, perm);
|
||||
return ret;
|
||||
}
|
||||
|
||||
OutputVector translate_flash_attn_ext(const NodeContext & context) {
|
||||
num_inputs_check(context, 4, 4);
|
||||
num_inputs_check(context, 3, 4);
|
||||
const bool has_mask = context.get_input_size() == 4;
|
||||
auto q_f32 = context.get_input(0);
|
||||
auto k = context.get_input(1);
|
||||
auto v = context.get_input(2);
|
||||
auto mask = context.get_input(3);
|
||||
const int op_case = context.get_op_case();
|
||||
|
||||
if (op_case == 1 || op_case == 2) {
|
||||
int64_t n_state_head = (int64_t) context.get_view_input_ggml_shape(1, 0)[3];
|
||||
int64_t n_head = (int64_t) context.get_view_input_ggml_shape(1, 0)[1];
|
||||
size_t nb1 = context.get_view_input_stride(1, 0)[2];
|
||||
size_t offset = context.get_view_input_offset(1, 0);
|
||||
ov::Output<ov::Node> attention_size;
|
||||
if (op_case == 1) {
|
||||
attention_size = context.get_input("attention_size");
|
||||
} else {
|
||||
attention_size = context.get_input("attention_size_static");
|
||||
}
|
||||
k = reshape_flat_kv(k, offset, nb1, n_head, n_state_head, attention_size);
|
||||
v = reshape_flat_kv(v, offset, nb1, n_head, n_state_head, attention_size);
|
||||
}
|
||||
|
||||
float * params = reinterpret_cast<float *>(context.get_output_op_params());
|
||||
float scale = params[0];
|
||||
@@ -43,16 +93,19 @@ OutputVector translate_flash_attn_ext(const NodeContext & context) {
|
||||
ov::Output<ov::Node> res;
|
||||
|
||||
// For stateful
|
||||
std::string mask_name = "KQ_mask_sliced";
|
||||
if (context.get_input_names()[3].find("swa") != std::string::npos) {
|
||||
mask_name = "KQ_mask_swa_sliced";
|
||||
}
|
||||
if (context.has_input(mask_name)) {
|
||||
mask = context.get_input(mask_name);
|
||||
}
|
||||
|
||||
if (mask.get_element_type() != ov::element::f16) {
|
||||
mask = std::make_shared<ov::op::v0::Convert>(mask, ov::element::f16);
|
||||
ov::Output<ov::Node> mask;
|
||||
if (has_mask) {
|
||||
mask = context.get_input(3);
|
||||
std::string mask_name = "KQ_mask_sliced";
|
||||
if (context.get_input_names()[3].find("swa") != std::string::npos) {
|
||||
mask_name = "KQ_mask_swa_sliced";
|
||||
}
|
||||
if (context.has_input(mask_name)) {
|
||||
mask = context.get_input(mask_name);
|
||||
}
|
||||
if (mask.get_element_type() != ov::element::f16) {
|
||||
mask = std::make_shared<ov::op::v0::Convert>(mask, ov::element::f16);
|
||||
}
|
||||
}
|
||||
|
||||
//auto tile_kv = [&](int64_t num_heads, int64_t num_heads_kv, int64_t head_size, ov::Output<Node> kv) {
|
||||
@@ -108,10 +161,14 @@ OutputVector translate_flash_attn_ext(const NodeContext & context) {
|
||||
// get [B, 1, 1, S_q, S_k], which NUMPY-broadcasts cleanly against the
|
||||
// [B, num_heads_kv, factor, S_q, S_k] scores: B==B, then 1→num_heads_kv and
|
||||
// 1→factor on the head dims.
|
||||
auto mask_unsq1 =
|
||||
std::make_shared<ov::op::v0::Unsqueeze>(mask, ov::op::v0::Constant::create(ov::element::i64, {1}, {2}));
|
||||
// mask_unsq1: [B, 1, 1, S_q, S_k] (rank 5)
|
||||
ov::Output<ov::Node> qk_masked = std::make_shared<ov::op::v1::Add>(qk_scaled, mask_unsq1);
|
||||
ov::Output<ov::Node> qk_masked;
|
||||
if (has_mask) {
|
||||
auto mask_unsq1 =
|
||||
std::make_shared<ov::op::v0::Unsqueeze>(mask, ov::op::v0::Constant::create(ov::element::i64, {1}, {2}));
|
||||
qk_masked = std::make_shared<ov::op::v1::Add>(qk_scaled, mask_unsq1);
|
||||
} else {
|
||||
qk_masked = qk_scaled;
|
||||
}
|
||||
|
||||
auto softmax = std::make_shared<ov::op::v8::Softmax>(qk_masked, /*axis=*/-1);
|
||||
|
||||
@@ -164,9 +221,16 @@ OutputVector translate_flash_attn_ext(const NodeContext & context) {
|
||||
k = tile_kv(num_heads, num_heads_kv, head_size, k);
|
||||
v = tile_kv(num_heads, num_heads_kv, head_size, v);
|
||||
|
||||
auto sdpa = std::make_shared<ov::op::v13::ScaledDotProductAttention>(q, k, v, mask, scale_node, false);
|
||||
res = std::make_shared<ov::op::v1::Transpose>(sdpa,
|
||||
ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}));
|
||||
constexpr auto causal = false;
|
||||
if (has_mask) {
|
||||
auto sdpa = std::make_shared<ov::op::v13::ScaledDotProductAttention>(q, k, v, mask, scale_node, causal);
|
||||
res = std::make_shared<ov::op::v1::Transpose>(
|
||||
sdpa, ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}));
|
||||
} else {
|
||||
auto sdpa = std::make_shared<ov::op::v13::ScaledDotProductAttention>(q, k, v, scale_node, causal);
|
||||
res = std::make_shared<ov::op::v1::Transpose>(
|
||||
sdpa, ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}));
|
||||
}
|
||||
res = std::make_shared<ov::op::v0::Convert>(res, ov::element::f32);
|
||||
return rename_outputs_with_suffix({res}, context.get_name());
|
||||
}
|
||||
|
||||
@@ -7,12 +7,15 @@
|
||||
#include <cmath>
|
||||
#include <cstdint>
|
||||
#include <memory>
|
||||
#include <numeric>
|
||||
#include <openvino/op/add.hpp>
|
||||
#include <openvino/op/broadcast.hpp>
|
||||
#include <openvino/op/concat.hpp>
|
||||
#include <openvino/op/constant.hpp>
|
||||
#include <openvino/op/convert.hpp>
|
||||
#include <openvino/op/exp.hpp>
|
||||
#include <openvino/op/gather.hpp>
|
||||
#include <openvino/op/less.hpp>
|
||||
#include <openvino/op/loop.hpp>
|
||||
#include <openvino/op/matmul.hpp>
|
||||
#include <openvino/op/multiply.hpp>
|
||||
@@ -80,6 +83,28 @@ OutputVector translate_gated_delta_net(const NodeContext & context) {
|
||||
g = std::make_shared<ov::op::v0::Squeeze>(g, ov::op::v0::Constant::create(ov::element::i64, {1}, {3}));
|
||||
beta = std::make_shared<ov::op::v0::Squeeze>(beta, ov::op::v0::Constant::create(ov::element::i64, {1}, {3}));
|
||||
|
||||
if (context.has_input("chunk_valid_len")) {
|
||||
// The last prefill chunk is padded with fabricated tokens. The recurrence is
|
||||
// S_t = S_{t-1} * exp(g_t) + k_t (x) ((v_t - S_{t-1}^T k_t) * beta_t)
|
||||
// so forcing g = 0 and beta = 0 makes a padded step an exact identity and keeps the final
|
||||
// state equal to the state after the last real token. Attention output at those positions
|
||||
// is garbage but never read.
|
||||
const auto & g_ps = g.get_partial_shape();
|
||||
FRONT_END_OP_CONVERSION_CHECK(g_ps.rank().is_static() && g_ps.rank().get_length() == 3 && g_ps[1].is_static(),
|
||||
"GATED_DELTA_NET pad masking requires a static token dimension");
|
||||
const int64_t n_tokens = g_ps[1].get_length();
|
||||
std::vector<int64_t> positions(n_tokens);
|
||||
std::iota(positions.begin(), positions.end(), 0);
|
||||
auto valid = std::make_shared<ov::op::v1::Less>(
|
||||
ov::op::v0::Constant::create(ov::element::i64, {(size_t) n_tokens}, positions),
|
||||
context.get_input("chunk_valid_len"));
|
||||
auto mask = std::make_shared<ov::op::v0::Unsqueeze>(
|
||||
std::make_shared<ov::op::v0::Convert>(valid, g.get_element_type()),
|
||||
ov::op::v0::Constant::create(ov::element::i64, {2}, std::vector<int64_t>{0, 2}));
|
||||
g = std::make_shared<ov::op::v1::Multiply>(g, mask);
|
||||
beta = std::make_shared<ov::op::v1::Multiply>(beta, mask);
|
||||
}
|
||||
|
||||
// std::cout << "GatedDeltaNet input shapes: q=" << q.get_partial_shape() << ", k=" << k.get_partial_shape()
|
||||
// << ", v=" << v.get_partial_shape() << ", g=" << g.get_partial_shape()
|
||||
// << ", beta=" << beta.get_partial_shape() << ", state=" << state.get_partial_shape() << std::endl;
|
||||
|
||||
@@ -0,0 +1,64 @@
|
||||
#include "../node_context.h"
|
||||
#include "../op_table.h"
|
||||
#include "../utils.h"
|
||||
|
||||
#include <memory>
|
||||
#include <openvino/core/node_output.hpp>
|
||||
#include <openvino/op/constant.hpp>
|
||||
#include <openvino/op/multiply.hpp>
|
||||
#include <openvino/op/sigmoid.hpp>
|
||||
#include <openvino/op/slice.hpp>
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace op {
|
||||
|
||||
OutputVector translate_glu_geglu_quick(const NodeContext & context) {
|
||||
num_inputs_check(context, 1, 2);
|
||||
|
||||
ov::Output<ov::Node> src0;
|
||||
ov::Output<ov::Node> src1;
|
||||
if (context.get_input_size() == 2) {
|
||||
src0 = process_view_input_new(context, 0);
|
||||
src1 = process_view_input_new(context, 1);
|
||||
} else {
|
||||
// split along last axis, nc = ne[0] / 2
|
||||
auto combined = process_view_input_new(context, 0);
|
||||
auto combined_shape = combined.get_partial_shape();
|
||||
int64_t last_dim_val = combined_shape[combined_shape.rank().get_length() - 1].get_length();
|
||||
int64_t nc = last_dim_val / 2;
|
||||
|
||||
auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
|
||||
auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
|
||||
auto start0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
|
||||
auto stop0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
|
||||
auto start1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
|
||||
auto stop1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc});
|
||||
|
||||
src0 = std::make_shared<ov::op::v8::Slice>(combined, start0, stop0, step, axis);
|
||||
src1 = std::make_shared<ov::op::v8::Slice>(combined, start1, stop1, step, axis);
|
||||
}
|
||||
|
||||
int32_t * params = context.get_output_op_params();
|
||||
const int32_t swapped = params[1];
|
||||
if (swapped) {
|
||||
std::swap(src0, src1);
|
||||
}
|
||||
|
||||
// GELU_QUICK(x) = x * sigmoid(1.702 * x)
|
||||
// Create the constant in the same type as src0 to avoid f16/f32 mismatch.
|
||||
auto input_type = src0.get_element_type();
|
||||
auto coef = ov::op::v0::Constant::create(input_type, ov::Shape{}, {1.702f});
|
||||
auto scaled = std::make_shared<ov::op::v1::Multiply>(src0, coef);
|
||||
auto sigmoid = std::make_shared<ov::op::v0::Sigmoid>(scaled);
|
||||
auto gated = std::make_shared<ov::op::v1::Multiply>(src0, sigmoid);
|
||||
auto res = std::make_shared<ov::op::v1::Multiply>(gated, src1);
|
||||
|
||||
return rename_outputs_with_suffix({res}, context.get_name());
|
||||
}
|
||||
|
||||
} // namespace op
|
||||
} // namespace ggml
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
@@ -0,0 +1,53 @@
|
||||
#include "../node_context.h"
|
||||
#include "../op_table.h"
|
||||
#include "../utils.h"
|
||||
|
||||
#include <openvino/op/avg_pool.hpp>
|
||||
#include <openvino/op/max_pool.hpp>
|
||||
#include <openvino/op/convert.hpp>
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace op {
|
||||
|
||||
OutputVector translate_pool_2d(const NodeContext & context) {
|
||||
num_inputs_check(context, 1, 1);
|
||||
const int32_t * params = context.get_output_op_params();
|
||||
|
||||
const int k0 = params[1];
|
||||
const int k1 = params[2];
|
||||
const int s0 = params[3];
|
||||
const int s1 = params[4];
|
||||
const int p0 = params[5];
|
||||
const int p1 = params[6];
|
||||
|
||||
const int op_case = context.get_op_case();
|
||||
ov::Output<Node> input = context.get_input(0);
|
||||
ov::Strides strides{static_cast<size_t>(s1), static_cast<size_t>(s0)};
|
||||
ov::Shape pads_begin{static_cast<size_t>(p1), static_cast<size_t>(p0)};
|
||||
ov::Shape pads_end{static_cast<size_t>(p1), static_cast<size_t>(p0)};
|
||||
ov::Shape kernel{static_cast<size_t>(k1), static_cast<size_t>(k0)};
|
||||
ov::Output<Node> res;
|
||||
|
||||
switch (op_case) {
|
||||
case 1: // GGML_OP_POOL_MAX
|
||||
{
|
||||
res = std::make_shared<ov::op::v1::MaxPool>(input, strides, pads_begin, pads_end, kernel);
|
||||
break;
|
||||
}
|
||||
case 2: // GGML_OP_POOL_AVG
|
||||
{
|
||||
res = std::make_shared<ov::op::v1::AvgPool>(input, strides, pads_begin, pads_end, kernel, false);
|
||||
break;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return rename_outputs_with_suffix({res}, context.get_name());
|
||||
}
|
||||
|
||||
} // namespace op
|
||||
} // namespace ggml
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
@@ -0,0 +1,36 @@
|
||||
#include "../node_context.h"
|
||||
#include "../op_table.h"
|
||||
#include "../utils.h"
|
||||
|
||||
#include <openvino/op/roll.hpp>
|
||||
#include <openvino/op/constant.hpp>
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace op {
|
||||
|
||||
OutputVector translate_roll(const NodeContext & context) {
|
||||
num_inputs_check(context, 1, 1);
|
||||
const int32_t * params = context.get_output_op_params();
|
||||
|
||||
int64_t s0 = params[0];
|
||||
int64_t s1 = params[1];
|
||||
int64_t s2 = params[2];
|
||||
int64_t s3 = params[3];
|
||||
|
||||
auto input = context.get_input(0);
|
||||
|
||||
auto shift = ov::op::v0::Constant::create(
|
||||
ov::element::i64, ov::Shape{4}, std::vector<int64_t>{s3, s2, s1, s0});
|
||||
auto axes = ov::op::v0::Constant::create(
|
||||
ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 1, 2, 3});
|
||||
|
||||
auto roll = std::make_shared<ov::op::v7::Roll>(input, shift, axes);
|
||||
return rename_outputs_with_suffix({roll}, context.get_name());
|
||||
}
|
||||
|
||||
} // namespace op
|
||||
} // namespace ggml
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
@@ -17,6 +17,13 @@ namespace op {
|
||||
OutputVector translate_view(const NodeContext & context) {
|
||||
num_inputs_check(context, 1, 1);
|
||||
|
||||
if (context.get_op_case() == 1) {
|
||||
// Static-mode identity pass-through for VIEWs over a GATED_DELTA_NET combined output or
|
||||
// the conv_input CONCAT; the consuming op (CPY/RMS_NORM) does its own runtime-correct
|
||||
// slicing on the full tensor (see ggml-decoder.cpp compute_op_case, GGML_OP_VIEW).
|
||||
return {context.get_input(0)};
|
||||
}
|
||||
|
||||
if (!context.is_static()) {
|
||||
// On the stateless/non-static path VIEW is normally a no-op (consumers re-slice).
|
||||
// EXCEPTION: the MoE expert aggregation slices each expert plane out of
|
||||
|
||||
@@ -10,6 +10,7 @@
|
||||
#include <openvino/op/matmul.hpp>
|
||||
#include <openvino/op/multiply.hpp>
|
||||
#include <openvino/op/negative.hpp>
|
||||
#include <openvino/op/relu.hpp>
|
||||
#include <openvino/op/sigmoid.hpp>
|
||||
#include <openvino/op/subtract.hpp>
|
||||
#include <openvino/op/tanh.hpp>
|
||||
@@ -55,10 +56,12 @@ std::unordered_map<std::string, CreatorFunction> get_supported_ops() {
|
||||
{"GGML_UNARY_OP_SIGMOID", op::translate_1to1_match_1_input<v0::Sigmoid> },
|
||||
{"GGML_UNARY_OP_EXP", op::translate_1to1_match_1_input<v0::Exp> },
|
||||
{"GGML_UNARY_OP_NEG", op::translate_1to1_match_1_input<v0::Negative> },
|
||||
{"GGML_UNARY_OP_RELU", op::translate_1to1_match_1_input<v0::Relu> },
|
||||
{"GGML_OP_VIEW", op::translate_view },
|
||||
{"GGML_GLU_OP_SWIGLU", op::translate_glu_swiglu },
|
||||
{"GGML_GLU_OP_SWIGLU_OAI", op::translate_glu_swiglu_oai },
|
||||
{"GGML_GLU_OP_GEGLU", op::translate_glu_geglu },
|
||||
{"GGML_GLU_OP_GEGLU_QUICK", op::translate_glu_geglu_quick },
|
||||
{"GGML_OP_SET_ROWS", op::translate_set_rows },
|
||||
{"GGML_OP_CPY", op::translate_cpy },
|
||||
{"GGML_OP_FLASH_ATTN_EXT", op::translate_flash_attn_ext },
|
||||
@@ -72,6 +75,8 @@ std::unordered_map<std::string, CreatorFunction> get_supported_ops() {
|
||||
{"GGML_OP_DIAG", op::translate_diag },
|
||||
{"GGML_OP_TRI", op::translate_tri },
|
||||
{"GGML_OP_SET", op::translate_set },
|
||||
{"GGML_OP_POOL_2D", op::translate_pool_2d },
|
||||
{"GGML_OP_ROLL", op::translate_roll },
|
||||
// solve_tri has accuracy issues on GPU
|
||||
// {"GGML_OP_SOLVE_TRI", op::translate_solve_tri },
|
||||
};
|
||||
|
||||
@@ -38,6 +38,7 @@ GGML_OP_CONVERTER(translate_view);
|
||||
GGML_OP_CONVERTER(translate_glu_swiglu);
|
||||
GGML_OP_CONVERTER(translate_glu_swiglu_oai);
|
||||
GGML_OP_CONVERTER(translate_glu_geglu);
|
||||
GGML_OP_CONVERTER(translate_glu_geglu_quick);
|
||||
GGML_OP_CONVERTER(translate_set_rows);
|
||||
GGML_OP_CONVERTER(translate_cpy);
|
||||
GGML_OP_CONVERTER(translate_argsort);
|
||||
@@ -53,6 +54,8 @@ GGML_OP_CONVERTER(translate_set);
|
||||
GGML_OP_CONVERTER(translate_diag);
|
||||
GGML_OP_CONVERTER(translate_tri);
|
||||
GGML_OP_CONVERTER(translate_solve_tri);
|
||||
GGML_OP_CONVERTER(translate_pool_2d);
|
||||
GGML_OP_CONVERTER(translate_roll);
|
||||
|
||||
} // namespace op
|
||||
|
||||
|
||||
@@ -0,0 +1,212 @@
|
||||
#include "fuse_to_conv.h"
|
||||
|
||||
#include <openvino/core/graph_util.hpp>
|
||||
#include <openvino/core/rt_info.hpp>
|
||||
#include <openvino/op/add.hpp>
|
||||
#include <openvino/op/constant.hpp>
|
||||
#include <openvino/op/convert.hpp>
|
||||
#include <openvino/op/convolution.hpp>
|
||||
#include <openvino/op/extractimagepatches.hpp>
|
||||
#include <openvino/op/matmul.hpp>
|
||||
#include <openvino/op/pad.hpp>
|
||||
#include <openvino/op/reshape.hpp>
|
||||
#include <openvino/op/transpose.hpp>
|
||||
#include <openvino/pass/pattern/op/label.hpp>
|
||||
#include <openvino/pass/pattern/op/pattern.hpp>
|
||||
#include <openvino/pass/pattern/op/wrap_type.hpp>
|
||||
|
||||
namespace opp = ov::pass::pattern;
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace pass {
|
||||
|
||||
// This pass fuses an IM2COL + MatMul convolution into OpenVINO's Convolution op for performance gains.
|
||||
// Reference the im2col.cpp translator for reference on the pattern being matched.
|
||||
|
||||
FuseToConv::FuseToConv() {
|
||||
const auto m_wei = opp::any_input();
|
||||
const auto m_act = opp::any_input();
|
||||
const auto m_matmul = opp::wrap_type<ov::op::v0::MatMul>({m_wei, m_act});
|
||||
|
||||
const auto callback = [=](ov::pass::pattern::Matcher & m) {
|
||||
const auto & pm = m.get_pattern_value_map();
|
||||
|
||||
auto matmul_node = ov::as_type_ptr<ov::op::v0::MatMul>(pm.at(m_matmul).get_node_shared_ptr());
|
||||
if (!matmul_node || matmul_node->get_transpose_a() || !matmul_node->get_transpose_b()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto trace = matmul_node->input_value(1);
|
||||
|
||||
// Optional Convert
|
||||
if (auto n = ov::as_type_ptr<ov::op::v0::Convert>(trace.get_node_shared_ptr())) {
|
||||
trace = n->input_value(0);
|
||||
}
|
||||
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
auto n = ov::as_type_ptr<ov::op::v1::Reshape>(trace.get_node_shared_ptr());
|
||||
if (!n) {
|
||||
return false;
|
||||
}
|
||||
trace = n->input_value(0);
|
||||
}
|
||||
|
||||
if (auto n = ov::as_type_ptr<ov::op::v1::Transpose>(trace.get_node_shared_ptr())) {
|
||||
trace = n->input_value(0);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (auto n = ov::as_type_ptr<ov::op::v1::Reshape>(trace.get_node_shared_ptr())) {
|
||||
trace = n->input_value(0);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (auto n = ov::as_type_ptr<ov::op::v1::Transpose>(trace.get_node_shared_ptr())) {
|
||||
trace = n->input_value(0);
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto eip = ov::as_type_ptr<ov::op::v3::ExtractImagePatches>(trace.get_node_shared_ptr());
|
||||
if (!eip) {
|
||||
return false;
|
||||
}
|
||||
const auto eip_strides = eip->get_strides(); // {stride_h, stride_w}
|
||||
const auto eip_rates = eip->get_rates(); // {dil_h, dil_w}
|
||||
|
||||
auto pad = ov::as_type_ptr<ov::op::v1::Pad>(eip->input_value(0).get_node_shared_ptr());
|
||||
if (!pad) {
|
||||
return false;
|
||||
}
|
||||
auto pads_begin_const =
|
||||
ov::as_type_ptr<ov::op::v0::Constant>(pad->input_value(1).get_node_shared_ptr());
|
||||
|
||||
const auto pads_begin_vals = pads_begin_const->cast_vector<int64_t>(); // {0, 0, pad_h, pad_w}
|
||||
const std::ptrdiff_t pad_h = static_cast<std::ptrdiff_t>(pads_begin_vals[2]);
|
||||
const std::ptrdiff_t pad_w = static_cast<std::ptrdiff_t>(pads_begin_vals[3]);
|
||||
|
||||
auto image_input = pad->input_value(0); // [N, IC, 1, IW] NCHW
|
||||
|
||||
auto w_trace = matmul_node->input_value(0);
|
||||
if (auto n = ov::as_type_ptr<ov::op::v0::Convert>(w_trace.get_node_shared_ptr())) {
|
||||
w_trace = n->input_value(0);
|
||||
}
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
auto n = ov::as_type_ptr<ov::op::v1::Reshape>(w_trace.get_node_shared_ptr());
|
||||
if (!n) {
|
||||
break;
|
||||
}
|
||||
w_trace = n->input_value(0);
|
||||
}
|
||||
|
||||
auto weight_const = ov::as_type_ptr<ov::op::v0::Constant>(w_trace.get_node_shared_ptr());
|
||||
if (!weight_const) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Reshape weight to [OC, IC, 1, KW] (OIHW).
|
||||
const auto w_shape = weight_const->get_shape();
|
||||
ov::Shape conv_w_shape;
|
||||
if (w_shape.size() == 3) {
|
||||
conv_w_shape = {w_shape[0], w_shape[1], 1, w_shape[2]};
|
||||
} else if (w_shape.size() == 4) {
|
||||
conv_w_shape = {w_shape[1], w_shape[2], 1, w_shape[3]};
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto weight_reshaped = register_new_node<ov::op::v0::Constant>(weight_const->get_element_type(), conv_w_shape,
|
||||
weight_const->get_data_ptr());
|
||||
|
||||
ov::Output<Node> weight_input = weight_reshaped;
|
||||
if (weight_reshaped->get_element_type() != image_input.get_element_type()) {
|
||||
weight_input = register_new_node<ov::op::v0::Convert>(weight_reshaped, image_input.get_element_type());
|
||||
}
|
||||
|
||||
auto conv = register_new_node<ov::op::v1::Convolution>(
|
||||
image_input, weight_input,
|
||||
ov::Strides{static_cast<size_t>(eip_strides[0]), static_cast<size_t>(eip_strides[1])},
|
||||
ov::CoordinateDiff{pad_h, pad_w}, ov::CoordinateDiff{pad_h, pad_w},
|
||||
ov::Strides{static_cast<size_t>(eip_rates[0]), static_cast<size_t>(eip_rates[1])},
|
||||
ov::op::PadType::EXPLICIT);
|
||||
|
||||
constexpr auto target_type = ov::element::f32;
|
||||
ov::Output<Node> conv_out = conv;
|
||||
if (conv_out.get_element_type() != target_type) {
|
||||
conv_out = register_new_node<ov::op::v0::Convert>(conv_out, target_type);
|
||||
}
|
||||
|
||||
std::shared_ptr<ov::op::v1::Add> add_node;
|
||||
ov::Output<Node> bias_input;
|
||||
for (const auto & consumer_in : matmul_node->output(0).get_target_inputs()) {
|
||||
auto cast = ov::as_type_ptr<ov::op::v0::Convert>(consumer_in.get_node()->shared_from_this());
|
||||
if (!cast) {
|
||||
continue;
|
||||
}
|
||||
for (const auto & add_in : cast->output(0).get_target_inputs()) {
|
||||
auto add = ov::as_type_ptr<ov::op::v1::Add>(add_in.get_node()->shared_from_this());
|
||||
if (!add) {
|
||||
continue;
|
||||
}
|
||||
for (size_t i = 0; i < 2; ++i) {
|
||||
if (ov::as_type_ptr<ov::op::v0::Constant>(add->input_value(i).get_node_shared_ptr())) {
|
||||
bias_input = add->input_value(i);
|
||||
add_node = add;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (add_node) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (add_node) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
ov::Output<Node> final_out;
|
||||
std::shared_ptr<Node> target_node;
|
||||
|
||||
if (add_node) {
|
||||
// Reshape bias [OC, 1] → [1, OC, 1, 1] for NCHW broadcasting.
|
||||
ov::Output<Node> bias = bias_input;
|
||||
if (bias.get_element_type() != target_type) {
|
||||
bias = register_new_node<ov::op::v0::Convert>(bias, target_type);
|
||||
}
|
||||
const auto oc = static_cast<int64_t>(conv_w_shape[0]);
|
||||
auto bias_shape = register_new_node<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4},
|
||||
std::vector<int64_t>{1, oc, 1, 1});
|
||||
bias = register_new_node<ov::op::v1::Reshape>(bias, bias_shape, false);
|
||||
final_out = register_new_node<ov::op::v1::Add>(conv_out, bias);
|
||||
target_node = add_node;
|
||||
} else {
|
||||
final_out = conv_out;
|
||||
target_node = matmul_node;
|
||||
}
|
||||
|
||||
// Reshape final output back to the target node's original shape if needed.
|
||||
auto orig_shape = target_node->get_output_partial_shape(0);
|
||||
if (orig_shape.is_static() && final_out.get_partial_shape() != orig_shape) {
|
||||
auto shape_const = register_new_node<ov::op::v0::Constant>(ov::element::i64, ov::Shape{orig_shape.size()},
|
||||
orig_shape.to_shape());
|
||||
final_out = register_new_node<ov::op::v1::Reshape>(final_out, shape_const, false);
|
||||
}
|
||||
|
||||
final_out.get_node_shared_ptr()->set_friendly_name(target_node->get_friendly_name());
|
||||
ov::copy_runtime_info(m.get_matched_nodes(), final_out.get_node_shared_ptr());
|
||||
ov::replace_node(target_node, final_out.get_node_shared_ptr());
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
register_matcher(std::make_shared<opp::Matcher>(m_matmul, "ov::frontend::ggml::pass::FuseToConv"), callback);
|
||||
}
|
||||
|
||||
} // namespace pass
|
||||
} // namespace ggml
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
@@ -0,0 +1,17 @@
|
||||
#include "openvino/pass/matcher_pass.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace ggml {
|
||||
namespace pass {
|
||||
|
||||
class FuseToConv : public ov::pass::MatcherPass {
|
||||
public:
|
||||
OPENVINO_MATCHER_PASS_RTTI("ov::frontend::ggml::pass::FuseToConv")
|
||||
FuseToConv();
|
||||
};
|
||||
|
||||
} // namespace pass
|
||||
} // namespace ggml
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
@@ -5,6 +5,7 @@
|
||||
#include "ggml-openvino/openvino/node_context.h"
|
||||
#include "ggml-openvino/openvino/utils.h"
|
||||
#include "input_model.h"
|
||||
#include "pass/fuse_to_conv.h"
|
||||
#include "pass/mark_decompression_convert_constant_folding.h"
|
||||
#include "pass/mark_dequantization_subgraph.h"
|
||||
#include "pass/squeeze_matmul.h"
|
||||
@@ -109,7 +110,8 @@ ov::pass::MakeStateful::ParamResPairs get_kv_param_res_pairs(
|
||||
void add_sliced_mask_stateful(TensorMap & tensor_map) {
|
||||
auto create_sliced_mask = [&](const std::string & mask_name, const std::string & sliced_name) {
|
||||
if ((tensor_map.find(mask_name) != tensor_map.end()) &&
|
||||
(tensor_map.find("token_len_per_seq") != tensor_map.end())) {
|
||||
(tensor_map.find("token_len_per_seq") != tensor_map.end()) &&
|
||||
(tensor_map.find("inp_pos") != tensor_map.end())) {
|
||||
auto token_len_per_seq = tensor_map.at("token_len_per_seq").get_node_shared_ptr();
|
||||
auto mask = tensor_map.at(mask_name).get_node_shared_ptr();
|
||||
std::shared_ptr<ov::Node> mask_sliced = mask;
|
||||
@@ -137,6 +139,7 @@ void add_sliced_mask_stateful(TensorMap & tensor_map) {
|
||||
};
|
||||
|
||||
create_sliced_mask("self_kq_mask", "KQ_mask_sliced");
|
||||
create_sliced_mask("KQ_mask", "KQ_mask_sliced");
|
||||
create_sliced_mask("self_kq_mask_swa", "KQ_mask_swa_sliced");
|
||||
}
|
||||
|
||||
@@ -395,6 +398,7 @@ std::shared_ptr<Model> TranslateSession::apply_transformations(std::shared_ptr<M
|
||||
// is_decompression_multiply() recognizes GatherMatmul as a valid consumer.
|
||||
manager.register_pass<ov::pass::MarkDequantization>(
|
||||
std::vector<ov::element::Type>{ov::element::u8, ov::element::i8, ov::element::u4, ov::element::i4});
|
||||
manager.register_pass<pass::FuseToConv>();
|
||||
|
||||
if (ggml_model_decoder->is_stateful()) {
|
||||
const auto kv_param_res_names = ggml_model_decoder->get_kv_param_res_names();
|
||||
|
||||
@@ -72,6 +72,7 @@ OutputVector rename_outputs_with_suffix(const OutputVector & outputs, const std:
|
||||
name += "_";
|
||||
name += suffix;
|
||||
node->set_friendly_name(name);
|
||||
// Uncomment to dump every node's inferred shape (used to hunt down dynamic dims on NPU).
|
||||
// std::cout << name << " " << output.get_partial_shape() << std::endl;
|
||||
}
|
||||
return outputs;
|
||||
|
||||
@@ -16,6 +16,8 @@
|
||||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <functional>
|
||||
#include <future>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
@@ -48,7 +50,7 @@ enum ggml_status ov_graph_compute(ggml_cgraph * cgraph, ggml_backend_t backend)
|
||||
GgmlOvDecoder::dump_cgraph(cgraph, filename);
|
||||
}
|
||||
|
||||
const auto is_static = ggml_openvino_is_npu();
|
||||
const auto is_static = ggml_openvino_is_npu() || ggml_openvino_getenv_int("GGML_OPENVINO_FORCE_STATIC");
|
||||
|
||||
GGML_ASSERT(ctx->runtime_context != nullptr);
|
||||
std::shared_ptr<ov_runtime_context> r_ctx = std::static_pointer_cast<ov_runtime_context>(ctx->runtime_context);
|
||||
@@ -168,13 +170,24 @@ ov::Tensor create_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
|
||||
|
||||
auto output_type = ggml_decoder->get_ov_type(ggml_tensor);
|
||||
ov::Shape output_shape;
|
||||
void * output_data = ggml_tensor->data;
|
||||
if (ggml_decoder->is_static()) {
|
||||
output_shape = infer_request->get_output_tensor(output_index).get_shape();
|
||||
} else {
|
||||
output_shape = ggml_decoder->get_shape(ggml_tensor);
|
||||
// For a CPY into a padded view_src (e.g. a padded KV cache buffer), the
|
||||
// OV ScatterUpdate node outputs the full view_src shape, not the CPY node's
|
||||
// own (smaller) shape. Using the CPY shape here causes set_output_tensor to
|
||||
// fail with a shape-incompatibility error. Use view_src's shape and data
|
||||
// pointer instead so the OV tensor matches the model output exactly.
|
||||
if (ggml_tensor->op == GGML_OP_CPY && ggml_tensor->view_src != nullptr &&
|
||||
ggml_nbytes(ggml_tensor) != ggml_nbytes(ggml_tensor->view_src)) {
|
||||
output_shape = ggml_decoder->get_shape(ggml_tensor->view_src);
|
||||
output_data = ggml_tensor->view_src->data;
|
||||
} else {
|
||||
output_shape = ggml_decoder->get_shape(ggml_tensor);
|
||||
}
|
||||
}
|
||||
|
||||
ov::Tensor output_tensor(output_type, output_shape, ggml_tensor->data);
|
||||
ov::Tensor output_tensor(output_type, output_shape, output_data);
|
||||
return output_tensor;
|
||||
}
|
||||
|
||||
@@ -583,7 +596,9 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
return chunk_size;
|
||||
};
|
||||
|
||||
static std::string device = "NPU";
|
||||
// Normally NPU, but honors GGML_OPENVINO_DEVICE so GGML_OPENVINO_FORCE_STATIC can run the
|
||||
// static-shape path on CPU/GPU to isolate translation bugs from NPUW/NPU-driver issues.
|
||||
static std::string device = ggml_openvino_get_device_name();
|
||||
static auto is_static = true;
|
||||
static auto stateful = false;
|
||||
|
||||
@@ -603,7 +618,7 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static);
|
||||
|
||||
const auto * inp_pos = get_inp_pos_tensor(cgraph);
|
||||
const auto is_prefill = get_is_prefill(inp_pos);
|
||||
const auto is_prefill = get_is_prefill(cgraph, inp_pos);
|
||||
graph_key key(cgraph);
|
||||
static const bool cache_enabled = !ggml_openvino_getenv_int("GGML_OPENVINO_DISABLE_CACHE");
|
||||
bool cache_hit = false;
|
||||
@@ -687,38 +702,55 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
stateful, false, false, prefill_chunk_size);
|
||||
decoder_end_time = ggml_time_us();
|
||||
|
||||
auto input_model_prefill = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder_prefill);
|
||||
auto input_model_decode = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder_decode);
|
||||
const bool dump_ir = ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_IR");
|
||||
const auto dump_ir_timestamp = static_cast<long long>(ggml_time_us());
|
||||
|
||||
auto model_prefill = ov::frontend::ggml::FrontEnd::convert(input_model_prefill);
|
||||
ggml_decoder_prefill->clear_model_weights();
|
||||
auto model_decode = ov::frontend::ggml::FrontEnd::convert(input_model_decode);
|
||||
ggml_decoder_decode->clear_model_weights();
|
||||
conversion_end_time = ggml_time_us();
|
||||
auto build_static_model = [&core, &config, dump_ir, dump_ir_timestamp](
|
||||
std::shared_ptr<GgmlOvDecoder> decoder,
|
||||
const char * tag,
|
||||
std::shared_ptr<ov::Model> & model,
|
||||
ov::CompiledModel & compiled_model,
|
||||
std::shared_ptr<ov::InferRequest> & infer_request,
|
||||
int64_t & local_conversion_end_time,
|
||||
int64_t & local_compile_end_time) {
|
||||
auto input_model = std::make_shared<ov::frontend::ggml::InputModel>(decoder);
|
||||
model = ov::frontend::ggml::FrontEnd::convert(input_model);
|
||||
decoder->clear_model_weights();
|
||||
local_conversion_end_time = ggml_time_us();
|
||||
|
||||
if (ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_IR")) {
|
||||
char timestamped_filename[64];
|
||||
auto timestamp = (long long) ggml_time_us();
|
||||
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_prefill_%lld.xml", timestamp);
|
||||
ov::serialize(model_prefill, timestamped_filename);
|
||||
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_decode_%lld.xml", timestamp);
|
||||
ov::serialize(model_decode, timestamped_filename);
|
||||
}
|
||||
if (dump_ir) {
|
||||
char timestamped_filename[64];
|
||||
snprintf(timestamped_filename, sizeof(timestamped_filename), "model_%s_%lld.xml", tag,
|
||||
dump_ir_timestamp);
|
||||
ov::serialize(model, timestamped_filename);
|
||||
}
|
||||
|
||||
compiled_model = core.compile_model(model, device, config);
|
||||
infer_request = std::make_shared<ov::InferRequest>(compiled_model.create_infer_request());
|
||||
local_compile_end_time = ggml_time_us();
|
||||
};
|
||||
std::shared_ptr<ov::Model> model_prefill;
|
||||
std::shared_ptr<ov::Model> model_decode;
|
||||
ov::CompiledModel compiled_model_prefill;
|
||||
ov::CompiledModel compiled_model_decode;
|
||||
auto remote_context = ggml_openvino_get_remote_context();
|
||||
if (remote_context.has_value()) {
|
||||
compiled_model_prefill = core.compile_model(model_prefill, remote_context.value(), config);
|
||||
compiled_model_decode = core.compile_model(model_decode, remote_context.value(), config);
|
||||
} else {
|
||||
compiled_model_prefill = core.compile_model(model_prefill, device, config);
|
||||
compiled_model_decode = core.compile_model(model_decode, device, config);
|
||||
}
|
||||
|
||||
auto infer_request_prefill = std::make_shared<ov::InferRequest>(compiled_model_prefill.create_infer_request());
|
||||
auto infer_request_decode = std::make_shared<ov::InferRequest>(compiled_model_decode.create_infer_request());
|
||||
compile_end_time = ggml_time_us();
|
||||
std::shared_ptr<ov::InferRequest> infer_request_prefill;
|
||||
std::shared_ptr<ov::InferRequest> infer_request_decode;
|
||||
int64_t prefill_conversion_end_time;
|
||||
int64_t decode_conversion_end_time;
|
||||
int64_t prefill_compile_end_time;
|
||||
int64_t decode_compile_end_time;
|
||||
auto prefill_future = std::async(std::launch::async, build_static_model, ggml_decoder_prefill, "prefill",
|
||||
std::ref(model_prefill), std::ref(compiled_model_prefill),
|
||||
std::ref(infer_request_prefill), std::ref(prefill_conversion_end_time),
|
||||
std::ref(prefill_compile_end_time));
|
||||
auto decode_future = std::async(std::launch::async, build_static_model, ggml_decoder_decode, "decode",
|
||||
std::ref(model_decode), std::ref(compiled_model_decode),
|
||||
std::ref(infer_request_decode), std::ref(decode_conversion_end_time),
|
||||
std::ref(decode_compile_end_time));
|
||||
prefill_future.get();
|
||||
decode_future.get();
|
||||
conversion_end_time = std::max(prefill_conversion_end_time, decode_conversion_end_time);
|
||||
compile_end_time = std::max(prefill_compile_end_time, decode_compile_end_time);
|
||||
|
||||
model = is_prefill ? model_prefill : model_decode;
|
||||
ggml_decoder = is_prefill ? ggml_decoder_prefill : ggml_decoder_decode;
|
||||
@@ -742,7 +774,7 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
}
|
||||
|
||||
if (is_prefill) {
|
||||
auto inp_len = inp_pos->ne[0];
|
||||
auto inp_len = get_inp_pos_n_tokens(cgraph, inp_pos);
|
||||
for (int chunk_index = 0; chunk_index * prefill_chunk_size < inp_len; chunk_index++) {
|
||||
for (size_t i = 0; i < ov_input_names_local.size(); i++) {
|
||||
auto param_name = ov_input_names_local[i];
|
||||
@@ -762,6 +794,11 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
continue;
|
||||
}
|
||||
auto * ggml_tensor = model_output_it->second;
|
||||
if (ggml_nbytes(ggml_tensor) == 0) {
|
||||
// Zero-row in-place writeback (e.g. the empty s_copy defrag remainder). The OV
|
||||
// Result is the full cache, so binding it over this 0-byte buffer overflows it.
|
||||
continue;
|
||||
}
|
||||
auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor);
|
||||
infer_request->set_output_tensor(i, output_tensor);
|
||||
}
|
||||
@@ -798,6 +835,9 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
|
||||
continue;
|
||||
}
|
||||
auto * ggml_tensor = model_output_it->second;
|
||||
if (ggml_nbytes(ggml_tensor) == 0) {
|
||||
continue;
|
||||
}
|
||||
auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor);
|
||||
infer_request->set_output_tensor(i, output_tensor);
|
||||
}
|
||||
@@ -1074,6 +1114,9 @@ ov::Tensor get_ov_input_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder, cons
|
||||
ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
|
||||
const std::string & param_name) {
|
||||
// NPU decoding stage
|
||||
if (ggml_decoder->get_model_extra_inputs().count(param_name)) {
|
||||
return get_ov_input_tensor(ggml_decoder, param_name);
|
||||
}
|
||||
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name);
|
||||
const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
|
||||
|
||||
@@ -1123,14 +1166,30 @@ ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr<GgmlOvDecoder> ggm
|
||||
const std::string & param_name,
|
||||
int chunk_index) {
|
||||
// NPU prompt processing stage
|
||||
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name);
|
||||
const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
|
||||
|
||||
const size_t input_len = ggml_decoder->get_input_len();
|
||||
const size_t chunk_size = ggml_decoder->m_prefill_chunk_size;
|
||||
const size_t chunk_valid_size = std::min(chunk_size, input_len - chunk_index * chunk_size);
|
||||
const size_t chunk_pad_size = chunk_size - chunk_valid_size;
|
||||
|
||||
if (param_name == "chunk_valid_len") {
|
||||
ov::Tensor input_tensor(ov::element::i64, ov::Shape{1});
|
||||
*input_tensor.data<int64_t>() = (int64_t) chunk_valid_size;
|
||||
return input_tensor;
|
||||
}
|
||||
if (chunk_index > 0 && param_name == "cache_rs_reset_len") {
|
||||
// The recurrent-state clear belongs to the start of the sequence. Re-applying it on every
|
||||
// chunk would wipe the state accumulated by the preceding chunks, so disable it (a zero
|
||||
// length makes scale.cpp's keep-mask select every slot) after the first chunk.
|
||||
ov::Tensor input_tensor(ov::element::i64, ov::Shape{1});
|
||||
*input_tensor.data<int64_t>() = 0;
|
||||
return input_tensor;
|
||||
}
|
||||
if (ggml_decoder->get_model_extra_inputs().count(param_name)) {
|
||||
return get_ov_input_tensor(ggml_decoder, param_name);
|
||||
}
|
||||
const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name);
|
||||
const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
|
||||
|
||||
if (GgmlOvDecoder::is_inp_pos(ggml_tensor, op) && GgmlOvDecoder::get_inp_pos_n_planes(op) > 1) {
|
||||
// IMROPE: inp_pos stacks n_planes (t/h/w/e) position planes, each of length
|
||||
// input_len; pad every plane independently so they stay aligned to chunk_size.
|
||||
@@ -1306,7 +1365,7 @@ void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor
|
||||
<< std::endl;
|
||||
switch (tensor.get_element_type()) {
|
||||
case ov::element::f32: {
|
||||
if (name.find("self_kq_mask") == std::string::npos) {
|
||||
if (name.find("self_kq_mask") == std::string::npos && name.find("KQ_mask") == std::string::npos) {
|
||||
std::cout << *(tensor.data<float>()) << std::endl;
|
||||
} else {
|
||||
size_t rows = tensor.get_shape()[2];
|
||||
@@ -1414,8 +1473,24 @@ const ggml_tensor * get_inp_pos_tensor(ggml_cgraph * cgraph) {
|
||||
throw std::runtime_error("get_inp_pos_tensor: inp_pos not found in cgraph");
|
||||
}
|
||||
|
||||
bool get_is_prefill(const ggml_tensor * inp_pos) {
|
||||
return inp_pos->ne[0] > 1;
|
||||
int64_t get_inp_pos_n_tokens(ggml_cgraph * cgraph, const ggml_tensor * inp_pos) {
|
||||
// IMROPE stacks n_planes (t/h/w/e) position planes into inp_pos, so ne[0] is
|
||||
// n_planes * n_tokens. Callers that need a token count must divide the planes out.
|
||||
int n_planes = 1;
|
||||
for (int i = 0; i < cgraph->n_nodes; ++i) {
|
||||
auto * op = cgraph->nodes[i];
|
||||
for (int j = 0; j < GGML_MAX_SRC; ++j) {
|
||||
if (op->src[j] == inp_pos) {
|
||||
n_planes = GgmlOvDecoder::get_inp_pos_n_planes(op);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
return inp_pos->ne[0] / n_planes;
|
||||
}
|
||||
|
||||
bool get_is_prefill(ggml_cgraph * cgraph, const ggml_tensor * inp_pos) {
|
||||
return get_inp_pos_n_tokens(cgraph, inp_pos) > 1;
|
||||
}
|
||||
|
||||
#pragma GCC diagnostic pop
|
||||
|
||||
@@ -164,7 +164,9 @@ std::vector<T> pad_input(const ggml_tensor * tensor, size_t padded_rows, size_t
|
||||
|
||||
const ggml_tensor * get_inp_pos_tensor(struct ggml_cgraph * cgraph);
|
||||
|
||||
bool get_is_prefill(const ggml_tensor * inp_pos);
|
||||
int64_t get_inp_pos_n_tokens(struct ggml_cgraph * cgraph, const ggml_tensor * inp_pos);
|
||||
|
||||
bool get_is_prefill(struct ggml_cgraph * cgraph, const ggml_tensor * inp_pos);
|
||||
|
||||
ov::Tensor get_ov_input_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & param_name);
|
||||
ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
#include <array>
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
@@ -150,7 +151,8 @@ struct sdpa_partition {
|
||||
|
||||
// Build + compile the contiguous-input GQA SDPA graph (MatMul->Divide->Add->SoftMax->MatMul), f32 out.
|
||||
// Mirrors the hardware-verified scratch/onednn_sdpa_probe.cpp build_gqa (partitions=1, sdp_primitive_kernel_t).
|
||||
static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int seq, int d) {
|
||||
static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int seq, int d,
|
||||
const std::array<int64_t, 5> & k_str, const std::array<int64_t, 5> & v_str) try {
|
||||
using ltype = logical_tensor::layout_type;
|
||||
using dt = logical_tensor::data_type;
|
||||
using ldims = logical_tensor::dims;
|
||||
@@ -158,11 +160,12 @@ static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int
|
||||
const int rep = H / Hkv;
|
||||
const ldims q_sz = {1, Hkv, rep, q, d}, kv_sz = {1, Hkv, 1, seq, d}, s_sz = {1, Hkv, rep, q, seq},
|
||||
sc = {1, 1, 1, 1, 1}, msk = {1, 1, 1, q, seq}, o_sz = {1, Hkv, rep, q, d};
|
||||
const ldims k_st(k_str.begin(), k_str.end()), v_st(v_str.begin(), v_str.end());
|
||||
int64_t id = 0;
|
||||
sdpa_partition E;
|
||||
|
||||
auto query = logical_tensor(id++, t, q_sz, ltype::strided);
|
||||
auto key = logical_tensor(id++, t, kv_sz, ltype::strided);
|
||||
auto key = logical_tensor(id++, t, kv_sz, k_st);
|
||||
auto score = logical_tensor(id++, fi, s_sz, ltype::strided);
|
||||
auto bmm1 = op(id++, op::kind::MatMul, "bmm1");
|
||||
bmm1.set_attr<bool>(op::attr::transpose_b, true); // key is [.., seq, d]
|
||||
@@ -184,7 +187,7 @@ static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int
|
||||
smax.set_attr<std::string>(op::attr::mode, "inf_as_zero");
|
||||
smax.add_inputs({masked}); smax.add_outputs({probs});
|
||||
|
||||
auto value = logical_tensor(id++, t, kv_sz, ltype::strided);
|
||||
auto value = logical_tensor(id++, t, kv_sz, v_st);
|
||||
// f16 output is REQUIRED to hit sdp_primitive_kernel_t (the systolic micro-kernel); an f32 output
|
||||
// falls to larger_partition_kernel_t which materializes N^2 (confirmed: scratch/onednn_sdpa_kernel_probe.cpp).
|
||||
// converted to the f32 ggml dst in the permute below.
|
||||
@@ -198,6 +201,7 @@ static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int
|
||||
|
||||
auto parts = g.get_partitions();
|
||||
if (parts.size() != 1 || !parts[0].is_supported()) {
|
||||
GGML_LOG_WARN("%s: oneDNN did not fuse the SDPA graph; falling back to TILE kernel\n", __func__);
|
||||
return E; // ok stays false -> caller falls back to TILE
|
||||
}
|
||||
E.ins = parts[0].get_input_ports();
|
||||
@@ -209,6 +213,12 @@ static sdpa_partition build_sdpa(const engine & eng, int H, int Hkv, int q, int
|
||||
E.ok = true;
|
||||
return E;
|
||||
}
|
||||
catch (const std::exception & e) {
|
||||
// compile() can reject a stride set the partitioner never inspects; memoise the failure so the
|
||||
// fallback costs one build rather than one per call.
|
||||
GGML_LOG_WARN("%s: oneDNN SDPA partition build failed (%s); falling back to TILE kernel\n", __func__, e.what());
|
||||
return {};
|
||||
}
|
||||
|
||||
void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) try {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
@@ -234,13 +244,34 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso
|
||||
ggml_sycl_pool_alloc<sycl::half> Qf(ctx.pool(), (size_t) H * q * d);
|
||||
cont_to_f16_sycl<float>((const char *) Q->data, Qf.get(), d, q, H, mb, Q->nb[1], Q->nb[2], Q->nb[3], stream);
|
||||
|
||||
// K/V: use pool-alloc for both F16 and dequant paths.
|
||||
// K/V: bind the f16 cache in place. llama.cpp permutes it to [token][head][dim], so its head
|
||||
// plane is strided rather than dense, which is what an explicit stride vector expresses.
|
||||
// Quantized and f32 KV still stage a dense copy -- the layout the k_str/v_str defaults describe.
|
||||
sycl::half * K_ptr = nullptr;
|
||||
sycl::half * V_ptr = nullptr;
|
||||
std::array<int64_t, 5> k_str{ Hkv * seq * d, seq * d, seq * d, d, 1 };
|
||||
std::array<int64_t, 5> v_str = k_str;
|
||||
std::optional<ggml_sycl_pool_alloc<sycl::half>> Kf_pool;
|
||||
std::optional<ggml_sycl_pool_alloc<sycl::half>> Vf_pool;
|
||||
|
||||
if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) {
|
||||
auto bindable = [](const ggml_tensor * t) {
|
||||
return t->nb[0] == sizeof(sycl::half) && t->nb[1] % sizeof(sycl::half) == 0 &&
|
||||
t->nb[2] % sizeof(sycl::half) == 0 && t->nb[3] % sizeof(sycl::half) == 0;
|
||||
};
|
||||
auto elem_strides = [](const ggml_tensor * t) {
|
||||
const int64_t s1 = (int64_t) (t->nb[1] / t->nb[0]);
|
||||
const int64_t s2 = (int64_t) (t->nb[2] / t->nb[0]);
|
||||
const int64_t s3 = (int64_t) (t->nb[3] / t->nb[0]);
|
||||
// dims are {mb=1, Hkv, rep=1, seq, d}; the size-1 dims at 0 and 2 never advance an address.
|
||||
return std::array<int64_t, 5>{ s3, s2, s2, s1, 1 };
|
||||
};
|
||||
|
||||
if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && bindable(K) && bindable(V)) {
|
||||
K_ptr = (sycl::half *) K->data;
|
||||
V_ptr = (sycl::half *) V->data;
|
||||
k_str = elem_strides(K);
|
||||
v_str = elem_strides(V);
|
||||
} else if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) {
|
||||
Kf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d);
|
||||
Vf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d);
|
||||
cont_to_f16_sycl<sycl::half>((const char *) K->data, Kf_pool->get(), d, seq, Hkv, mb, K->nb[1], K->nb[2], K->nb[3], stream);
|
||||
@@ -341,19 +372,24 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso
|
||||
|
||||
ggml_sycl_pool_alloc<sycl::half> outf(ctx.pool(), (size_t) H * q * d); // f16 contiguous SDPA out [mb,H,q,d]
|
||||
|
||||
// compile once per (device, shape), reuse across layers/calls.
|
||||
// compile once per (device, shape, KV strides), reuse across layers/calls. Stride 2 always
|
||||
// repeats stride 1 and stride 4 is always 1, so the key covers every entry that can differ.
|
||||
static std::unordered_map<std::string, sdpa_partition> cache;
|
||||
char keyb[96];
|
||||
snprintf(keyb, sizeof(keyb), "%d:%lld:%lld:%lld:%lld:%lld", ggml_sycl_get_device(),
|
||||
(long long) H, (long long) Hkv, (long long) q, (long long) seq, (long long) d);
|
||||
char keyb[256];
|
||||
snprintf(keyb, sizeof(keyb), "%d:%lld:%lld:%lld:%lld:%lld:%lld:%lld:%lld:%lld:%lld:%lld", ggml_sycl_get_device(),
|
||||
(long long) H, (long long) Hkv, (long long) q, (long long) seq, (long long) d,
|
||||
(long long) k_str[0], (long long) k_str[1], (long long) k_str[3],
|
||||
(long long) v_str[0], (long long) v_str[1], (long long) v_str[3]);
|
||||
auto it = cache.find(keyb);
|
||||
if (it == cache.end()) {
|
||||
it = cache.emplace(keyb, build_sdpa(eng, (int) H, (int) Hkv, (int) q, (int) seq, (int) d)).first;
|
||||
it = cache.emplace(keyb, build_sdpa(eng, (int) H, (int) Hkv, (int) q, (int) seq, (int) d, k_str, v_str)).first;
|
||||
}
|
||||
sdpa_partition & E = it->second;
|
||||
// _supported() is authoritative: if it accepted this op the partition must build.
|
||||
// A failure here is a gap in _supported() -- surface it, don't mask it with a fallback.
|
||||
GGML_ASSERT(E.ok && "oneDNN SDPA partition failed to build for a _supported() shape");
|
||||
if (!E.ok) {
|
||||
// oneDNN can decline a shape or a stride set that _supported() never sees; build_sdpa warns per key.
|
||||
ggml_sycl_flash_attn_ext_tile(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
auto id2ptr = [&](size_t r) -> void * {
|
||||
if (r == E.id_q) return Qf.get();
|
||||
|
||||
@@ -104,7 +104,6 @@ enum best_fattn_kernel {
|
||||
|
||||
|
||||
static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const ggml_tensor * dst) {
|
||||
GGML_UNUSED(device);
|
||||
#ifndef SYCL_FLASH_ATTN
|
||||
GGML_UNUSED(dst);
|
||||
return BEST_FATTN_KERNEL_NONE;
|
||||
@@ -263,6 +262,11 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const
|
||||
}
|
||||
} else {
|
||||
if (Q->ne[1] <= 2) {
|
||||
// TILE is faster for quantized KV decode on Xe2 (BMG); keep VEC on untested archs
|
||||
const gpu_arch arch = ggml_sycl_info().devices[device].hw_info.arch;
|
||||
if (arch == gpu_arch::intel_gpu_bmg_g21 || arch == gpu_arch::intel_gpu_bmg_g31) {
|
||||
return BEST_FATTN_KERNEL_TILE;
|
||||
}
|
||||
return BEST_FATTN_KERNEL_VEC;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -467,10 +467,15 @@ class GGUFWriter:
|
||||
shard_bar.reset(total=(total if total > 0 else None))
|
||||
|
||||
# relying on the fact that Python dicts preserve insertion order (since 3.7)
|
||||
for ti in tensors.values():
|
||||
for name, ti in tensors.items():
|
||||
assert ti.tensor is not None # can only iterate once over the tensors
|
||||
assert ti.tensor.nbytes == ti.nbytes
|
||||
start = fout.tell()
|
||||
ti.tensor.tofile(fout)
|
||||
# a short write here would only surface as a corrupt file at load time
|
||||
if fout.tell() - start != ti.nbytes:
|
||||
raise ValueError(
|
||||
f"tensor {name!r} wrote {fout.tell() - start} bytes, expected {ti.nbytes}")
|
||||
if shard_bar is not None:
|
||||
shard_bar.update(ti.nbytes)
|
||||
if bar is not None:
|
||||
|
||||
@@ -251,6 +251,10 @@ class LazyChunkedTensor:
|
||||
def numpy(self) -> LazyChunkedTensor:
|
||||
return self
|
||||
|
||||
def __array__(self, *args, **kwargs):
|
||||
# numpy would otherwise make a 1-element object array of self, and write 8 bytes
|
||||
raise TypeError("LazyChunkedTensor cannot become an ndarray, it is written in chunks")
|
||||
|
||||
def quantize(self, qtype: Any) -> LazyChunkedTensor:
|
||||
from .constants import GGMLQuantizationType
|
||||
from .quants import QuantError, quant_shape_to_byte_shape
|
||||
|
||||
@@ -1100,6 +1100,7 @@ bool llm_arch_supports_rs_rollback(const llm_arch & arch) {
|
||||
switch (arch) {
|
||||
case LLM_ARCH_QWEN35:
|
||||
case LLM_ARCH_QWEN35MOE:
|
||||
case LLM_ARCH_QWEN4EXP:
|
||||
case LLM_ARCH_DEEPSEEK4:
|
||||
case LLM_ARCH_NEMOTRON_H:
|
||||
case LLM_ARCH_NEMOTRON_H_MOE:
|
||||
@@ -1141,6 +1142,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
case LLM_ARCH_BAILINGMOE3:
|
||||
case LLM_ARCH_KIMI_K3:
|
||||
case LLM_ARCH_QWEN4EXP:
|
||||
case LLM_ARCH_QWEN3TTS:
|
||||
return false;
|
||||
default:
|
||||
|
||||
+323
-122
@@ -9,7 +9,9 @@
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <iterator>
|
||||
#include <limits>
|
||||
#include <stdexcept>
|
||||
#include <tuple>
|
||||
|
||||
//
|
||||
// llama_memory_hybrid_idx
|
||||
@@ -47,6 +49,7 @@ llama_memory_hybrid_idx::llama_memory_hybrid_idx(
|
||||
hparams_idx(model.hparams),
|
||||
mem_idx(filter_idx == nullptr ? nullptr : [&] {
|
||||
// MQA with a single key head of indexer_head_size, as llama_kv_cache_dsa shapes its own
|
||||
hparams_idx.rope_type = LLAMA_ROPE_TYPE_NONE;
|
||||
std::fill(hparams_idx.n_head_kv_arr.begin(), hparams_idx.n_head_kv_arr.end(), 1);
|
||||
hparams_idx.n_embd_head_k_full = model.hparams.indexer_head_size;
|
||||
|
||||
@@ -137,6 +140,8 @@ void llama_memory_hybrid_idx::clear(bool data) {
|
||||
if (mem_idx) {
|
||||
mem_idx->clear(data);
|
||||
}
|
||||
|
||||
qsa_histories.clear();
|
||||
}
|
||||
|
||||
bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
||||
@@ -149,15 +154,96 @@ bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_po
|
||||
mem_idx->seq_rm(seq_id, p0, p1);
|
||||
}
|
||||
|
||||
return get_mem_attn()->seq_rm(seq_id, p0, p1);
|
||||
const bool res = get_mem_attn()->seq_rm(seq_id, p0, p1);
|
||||
if (!res) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto remove = [&](qsa_history & history) {
|
||||
history.erase(std::remove_if(history.begin(), history.end(), [&](const qsa_token & token) {
|
||||
return (p0 < 0 || token.pos[0] >= p0) && (p1 < 0 || token.pos[0] < p1);
|
||||
}), history.end());
|
||||
};
|
||||
|
||||
if (seq_id < 0) {
|
||||
for (auto & item : qsa_histories) {
|
||||
remove(item.second);
|
||||
}
|
||||
} else {
|
||||
auto it = qsa_histories.find(seq_id);
|
||||
if (it != qsa_histories.end()) {
|
||||
remove(it->second);
|
||||
if (it->second.empty()) {
|
||||
qsa_histories.erase(it);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
||||
if (seq_id_src == seq_id_dst) {
|
||||
return;
|
||||
}
|
||||
|
||||
qsa_history copied;
|
||||
const auto & cells_src = get_mem_attn()->get_cells(seq_id_src);
|
||||
const auto & cells_dst = get_mem_attn()->get_cells(seq_id_dst);
|
||||
const bool replace = &cells_src != &cells_dst;
|
||||
const auto src = qsa_histories.find(seq_id_src);
|
||||
if (src != qsa_histories.end()) {
|
||||
using pos_key = std::tuple<llama_pos, llama_pos, llama_pos>;
|
||||
std::map<pos_key, std::vector<bool>> cells_by_pos;
|
||||
for (uint32_t cell = 0; cell < cells_src.size(); ++cell) {
|
||||
if (cells_src.is_empty(cell) || !cells_src.seq_has(cell, seq_id_src)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const llama_pos pos = cells_src.pos_get(cell);
|
||||
if ((p0 >= 0 && pos < p0) || (p1 >= 0 && pos >= p1)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const auto & ext = cells_src.ext_get(cell);
|
||||
cells_by_pos[{ pos, ext.y, ext.x }].push_back(!replace && cells_src.seq_has(cell, seq_id_dst));
|
||||
}
|
||||
|
||||
std::map<pos_key, size_t> next_cell;
|
||||
for (const auto & token : src->second) {
|
||||
if ((p0 >= 0 && token.pos[0] < p0) || (p1 >= 0 && token.pos[0] >= p1)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const pos_key key = { token.pos[0], token.pos[1], token.pos[2] };
|
||||
auto cells = cells_by_pos.find(key);
|
||||
if (cells == cells_by_pos.end()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
size_t & index = next_cell[key];
|
||||
if (index < cells->second.size() && !cells->second[index++]) {
|
||||
copied.push_back(token);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
llama_memory_hybrid::seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
|
||||
if (mem_idx) {
|
||||
mem_idx->seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
}
|
||||
|
||||
if (replace) {
|
||||
if (copied.empty()) {
|
||||
qsa_histories.erase(seq_id_dst);
|
||||
} else {
|
||||
qsa_histories[seq_id_dst] = std::move(copied);
|
||||
}
|
||||
} else if (!copied.empty()) {
|
||||
auto & dst = qsa_histories[seq_id_dst];
|
||||
dst.insert(dst.end(), copied.begin(), copied.end());
|
||||
}
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::seq_keep(llama_seq_id seq_id) {
|
||||
@@ -166,6 +252,13 @@ void llama_memory_hybrid_idx::seq_keep(llama_seq_id seq_id) {
|
||||
if (mem_idx) {
|
||||
mem_idx->seq_keep(seq_id);
|
||||
}
|
||||
|
||||
auto it = qsa_histories.find(seq_id);
|
||||
qsa_history keep = it == qsa_histories.end() ? qsa_history{} : std::move(it->second);
|
||||
qsa_histories.clear();
|
||||
if (!keep.empty()) {
|
||||
qsa_histories.emplace(seq_id, std::move(keep));
|
||||
}
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
|
||||
@@ -174,6 +267,15 @@ void llama_memory_hybrid_idx::seq_add(llama_seq_id seq_id, llama_pos p0, llama_p
|
||||
if (mem_idx) {
|
||||
mem_idx->seq_add(seq_id, p0, p1, shift);
|
||||
}
|
||||
|
||||
auto it = qsa_histories.find(seq_id);
|
||||
if (it != qsa_histories.end()) {
|
||||
for (auto & token : it->second) {
|
||||
if ((p0 < 0 || token.pos[0] >= p0) && (p1 < 0 || token.pos[0] < p1)) {
|
||||
token.pos[0] += shift;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
|
||||
@@ -182,6 +284,15 @@ void llama_memory_hybrid_idx::seq_div(llama_seq_id seq_id, llama_pos p0, llama_p
|
||||
if (mem_idx) {
|
||||
mem_idx->seq_div(seq_id, p0, p1, d);
|
||||
}
|
||||
|
||||
auto it = qsa_histories.find(seq_id);
|
||||
if (it != qsa_histories.end()) {
|
||||
for (auto & token : it->second) {
|
||||
if ((p0 < 0 || token.pos[0] >= p0) && (p1 < 0 || token.pos[0] < p1)) {
|
||||
token.pos[0] /= d;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid_idx::memory_breakdown() const {
|
||||
@@ -205,8 +316,28 @@ void llama_memory_hybrid_idx::state_write(llama_io_write_i & io, llama_seq_id se
|
||||
if (mem_idx) {
|
||||
mem_idx->state_write(io, seq_id, flags);
|
||||
}
|
||||
}
|
||||
|
||||
uint32_t n_histories = 0;
|
||||
if (seq_id < 0) {
|
||||
n_histories = (uint32_t) qsa_histories.size();
|
||||
} else if (qsa_histories.count(seq_id) != 0) {
|
||||
n_histories = 1;
|
||||
}
|
||||
io.write(&n_histories, sizeof(n_histories));
|
||||
|
||||
for (const auto & item : qsa_histories) {
|
||||
if (seq_id >= 0 && item.first != seq_id) {
|
||||
continue;
|
||||
}
|
||||
|
||||
io.write(&item.first, sizeof(item.first));
|
||||
const uint64_t n_tokens = item.second.size();
|
||||
io.write(&n_tokens, sizeof(n_tokens));
|
||||
for (const auto & token : item.second) {
|
||||
io.write(token.pos.data(), sizeof(token.pos));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
|
||||
@@ -230,6 +361,34 @@ void llama_memory_hybrid_idx::state_read(llama_io_read_i & io, llama_seq_id seq_
|
||||
if (mem_idx) {
|
||||
mem_idx->state_read_sinfo(io, seq_id, flags, nullptr, &sinfos_attn);
|
||||
}
|
||||
|
||||
uint32_t n_histories;
|
||||
io.read(&n_histories, sizeof(n_histories));
|
||||
if (n_histories > LLAMA_MAX_SEQ) {
|
||||
throw std::runtime_error("invalid QSA history count");
|
||||
}
|
||||
|
||||
if (seq_id < 0) {
|
||||
qsa_histories.clear();
|
||||
} else {
|
||||
qsa_histories.erase(seq_id);
|
||||
}
|
||||
|
||||
for (uint32_t ih = 0; ih < n_histories; ++ih) {
|
||||
llama_seq_id stored_seq;
|
||||
uint64_t n_tokens;
|
||||
io.read(&stored_seq, sizeof(stored_seq));
|
||||
io.read(&n_tokens, sizeof(n_tokens));
|
||||
if (stored_seq < 0 || stored_seq >= LLAMA_MAX_SEQ || n_tokens > get_mem_attn()->get_size()) {
|
||||
throw std::runtime_error("invalid QSA history");
|
||||
}
|
||||
|
||||
auto & history = qsa_histories[seq_id < 0 ? stored_seq : seq_id];
|
||||
history.resize(n_tokens);
|
||||
for (auto & token : history) {
|
||||
io.read(token.pos.data(), sizeof(token.pos));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} catch (...) {
|
||||
@@ -255,12 +414,156 @@ void llama_memory_hybrid_idx::state_drop(llama_seq_id seq_id) {
|
||||
if (mem_idx) {
|
||||
mem_idx->seq_rm(seq_id, -1, -1);
|
||||
}
|
||||
|
||||
qsa_histories.erase(seq_id);
|
||||
}
|
||||
|
||||
llama_kv_cache * llama_memory_hybrid_idx::get_mem_idx() const {
|
||||
return mem_idx.get();
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::commit_qsa_tokens(const llama_ubatch & ubatch) {
|
||||
if (!mem_idx) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
|
||||
qsa_token token = {};
|
||||
if (ubatch.token) {
|
||||
token.pos = { ubatch.pos[i], ubatch.pos[i], ubatch.pos[i], 0 };
|
||||
} else {
|
||||
for (uint32_t ip = 0; ip < token.pos.size(); ++ip) {
|
||||
token.pos[ip] = ip < ubatch.n_pos ? ubatch.pos[i + ip*ubatch.n_tokens] : ubatch.pos[i];
|
||||
}
|
||||
}
|
||||
|
||||
for (int32_t is = 0; is < ubatch.n_seq_id[i]; ++is) {
|
||||
qsa_histories[ubatch.seq_id[i][is]].push_back(token);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx::set_input_qsa(
|
||||
ggml_tensor * block_cells,
|
||||
ggml_tensor * block_pos,
|
||||
ggml_tensor * block_mask,
|
||||
ggml_tensor * selected,
|
||||
const ggml_tensor * kq_mask,
|
||||
const llama_ubatch * ubatch,
|
||||
uint32_t ratio,
|
||||
uint32_t block_topk) const {
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(block_cells->buffer));
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(block_pos->buffer));
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(block_mask->buffer));
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(selected->buffer));
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
|
||||
|
||||
const int64_t n_blocks = block_cells->ne[1];
|
||||
const int64_t n_tokens = ubatch->n_tokens;
|
||||
const int64_t n_pos = block_pos->ne[1];
|
||||
const int64_t n_kv = selected->ne[0];
|
||||
|
||||
GGML_ASSERT(block_cells->type == GGML_TYPE_I32);
|
||||
GGML_ASSERT(block_pos->type == GGML_TYPE_I32);
|
||||
GGML_ASSERT(block_mask->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(selected->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(block_cells->ne[0] == ratio && block_cells->ne[2] == n_tokens);
|
||||
GGML_ASSERT(block_pos->ne[0] == n_blocks && block_pos->ne[2] == n_tokens);
|
||||
GGML_ASSERT(block_mask->ne[0] == n_blocks && block_mask->ne[1] == n_tokens);
|
||||
GGML_ASSERT(selected->ne[1] == n_tokens);
|
||||
GGML_ASSERT(kq_mask->ne[0] == n_kv);
|
||||
|
||||
int32_t * cell_data = (int32_t *) block_cells->data;
|
||||
int32_t * pos_data = (int32_t *) block_pos->data;
|
||||
float * mask_data = (float *) block_mask->data;
|
||||
float * selected_data = (float *) selected->data;
|
||||
|
||||
std::fill(cell_data, cell_data + ggml_nelements(block_cells), 0);
|
||||
std::fill(pos_data, pos_data + ggml_nelements(block_pos), 0);
|
||||
std::fill(mask_data, mask_data + ggml_nelements(block_mask), -INFINITY);
|
||||
std::fill(selected_data, selected_data + ggml_nelements(selected), 0.0f);
|
||||
|
||||
auto mask_visible = [&](int64_t query, uint32_t cell) {
|
||||
const int64_t index = query*n_kv + cell;
|
||||
if (kq_mask->type == GGML_TYPE_F16) {
|
||||
return std::isfinite(ggml_fp16_to_fp32(((const ggml_fp16_t *) kq_mask->data)[index]));
|
||||
}
|
||||
return std::isfinite(((const float *) kq_mask->data)[index]);
|
||||
};
|
||||
|
||||
for (int64_t iq = 0; iq < n_tokens; ++iq) {
|
||||
const llama_seq_id seq_id = ubatch->seq_id[iq][0];
|
||||
const auto found = qsa_histories.find(seq_id);
|
||||
if (found == qsa_histories.end()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const auto & cells = get_mem_attn()->get_cells(seq_id);
|
||||
using pos_key = std::tuple<llama_pos, llama_pos, llama_pos>;
|
||||
std::map<pos_key, std::vector<uint32_t>> cells_by_pos;
|
||||
for (uint32_t cell = 0; cell < cells.size() && cell < (uint32_t) n_kv; ++cell) {
|
||||
if (cells.is_empty(cell) || !cells.seq_has(cell, seq_id)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const auto & ext = cells.ext_get(cell);
|
||||
cells_by_pos[{ cells.pos_get(cell), ext.y, ext.x }].push_back(cell);
|
||||
}
|
||||
|
||||
std::map<pos_key, size_t> next_cell;
|
||||
std::vector<std::pair<const qsa_token *, uint32_t>> visible;
|
||||
for (const auto & token : found->second) {
|
||||
const pos_key key = { token.pos[0], token.pos[1], token.pos[2] };
|
||||
auto cells = cells_by_pos.find(key);
|
||||
if (cells == cells_by_pos.end()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
size_t & index = next_cell[key];
|
||||
if (index >= cells->second.size()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const uint32_t cell = cells->second[index++];
|
||||
if (mask_visible(iq, cell)) {
|
||||
visible.emplace_back(&token, cell);
|
||||
}
|
||||
}
|
||||
|
||||
const size_t n_complete = visible.size()/ratio;
|
||||
const size_t n_write = std::min<size_t>(n_complete, n_blocks);
|
||||
std::vector<uint8_t> used_cells(n_kv, 0);
|
||||
for (size_t ib = 0; ib < n_write; ++ib) {
|
||||
mask_data[iq*n_blocks + ib] = 0.0f;
|
||||
for (uint32_t ir = 0; ir < ratio; ++ir) {
|
||||
const uint32_t cell = visible[ib*ratio + ir].second;
|
||||
cell_data[(iq*n_blocks + ib)*ratio + ir] = cell;
|
||||
used_cells[cell] = 1;
|
||||
}
|
||||
for (int64_t ip = 0; ip < n_pos; ++ip) {
|
||||
pos_data[(iq*n_pos + ip)*n_blocks + ib] = visible[ib*ratio].first->pos[ip];
|
||||
}
|
||||
}
|
||||
|
||||
uint32_t fallback_cell = 0;
|
||||
for (size_t ib = n_write; ib < (size_t) n_blocks; ++ib) {
|
||||
for (uint32_t ir = 0; ir < ratio; ++ir) {
|
||||
while (fallback_cell < used_cells.size() && used_cells[fallback_cell]) {
|
||||
++fallback_cell;
|
||||
}
|
||||
GGML_ASSERT(fallback_cell < used_cells.size());
|
||||
cell_data[(iq*n_blocks + ib)*ratio + ir] = fallback_cell;
|
||||
used_cells[fallback_cell++] = 1;
|
||||
}
|
||||
}
|
||||
|
||||
const size_t selected_start = n_complete <= block_topk ? 0 : n_complete*ratio;
|
||||
for (size_t iv = selected_start; iv < visible.size(); ++iv) {
|
||||
selected_data[iq*n_kv + visible[iv].second] = 1.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
// llama_memory_hybrid_idx_context
|
||||
//
|
||||
@@ -295,7 +598,9 @@ llama_memory_hybrid_idx_context::llama_memory_hybrid_idx_context(
|
||||
llama_context * lctx,
|
||||
bool optimize) :
|
||||
llama_memory_hybrid_context(mem, lctx, optimize),
|
||||
mem(mem) {}
|
||||
mem(mem),
|
||||
ctx_idx(mem->get_mem_idx() == nullptr ? nullptr :
|
||||
mem->get_mem_idx()->init_update(lctx, optimize)) {}
|
||||
|
||||
llama_memory_hybrid_idx_context::llama_memory_hybrid_idx_context(
|
||||
llama_memory_hybrid_idx * mem,
|
||||
@@ -307,7 +612,8 @@ llama_memory_hybrid_idx_context::llama_memory_hybrid_idx_context(
|
||||
mem(mem),
|
||||
ns_ubatch(llama_memory_hybrid_idx_ns(sinfos_idx)),
|
||||
ctx_idx(mem->get_mem_idx() == nullptr ? nullptr :
|
||||
new llama_kv_cache_context(mem->get_mem_idx(), std::move(sinfos_idx), ubatches)) {}
|
||||
new llama_kv_cache_context(mem->get_mem_idx(), std::move(sinfos_idx), ubatches)),
|
||||
has_ubatches(true) {}
|
||||
|
||||
bool llama_memory_hybrid_idx_context::next() {
|
||||
if (ctx_idx) {
|
||||
@@ -326,6 +632,10 @@ bool llama_memory_hybrid_idx_context::apply() {
|
||||
res = res & ctx_idx->apply();
|
||||
}
|
||||
|
||||
if (res && ctx_idx && has_ubatches) {
|
||||
mem->commit_qsa_tokens(ctx_idx->get_ubatch());
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
@@ -340,126 +650,17 @@ uint32_t llama_memory_hybrid_idx_context::get_n_stream() const {
|
||||
}
|
||||
|
||||
void llama_memory_hybrid_idx_context::set_input_qsa(
|
||||
ggml_tensor * cell_blk,
|
||||
ggml_tensor * blk_cells,
|
||||
ggml_tensor * blk_pos,
|
||||
ggml_tensor * bias,
|
||||
ggml_tensor * block_cells,
|
||||
ggml_tensor * block_pos,
|
||||
ggml_tensor * block_mask,
|
||||
ggml_tensor * selected,
|
||||
const ggml_tensor * kq_mask,
|
||||
const llama_ubatch * ubatch,
|
||||
uint32_t ratio,
|
||||
bool blk_bias) const {
|
||||
uint32_t ratio,
|
||||
uint32_t block_topk) const {
|
||||
GGML_ASSERT(ratio > 0);
|
||||
GGML_ASSERT(mem != nullptr && mem->get_mem_idx() != nullptr);
|
||||
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(cell_blk->buffer));
|
||||
|
||||
const int64_t n_kv = cell_blk->ne[0];
|
||||
const int64_t n_ns = cell_blk->ne[1]; // streams in this ubatch
|
||||
const int64_t n_blocks = blk_pos->ne[0]/(4*n_ns);
|
||||
const int64_t n_tokens = ubatch->n_tokens;
|
||||
const int64_t r = ratio;
|
||||
|
||||
GGML_ASSERT(n_tokens % n_ns == 0);
|
||||
const int64_t n_tps = n_tokens/n_ns; // tokens per stream
|
||||
|
||||
int32_t * dst_cell_blk = (int32_t *) cell_blk->data;
|
||||
int32_t * dst_blk_cells = (int32_t *) blk_cells->data;
|
||||
int32_t * dst_blk_pos = (int32_t *) blk_pos->data;
|
||||
float * dst_bias = (float *) bias->data;
|
||||
|
||||
// block b covers [b*ratio, (b+1)*ratio), so its first token is at b*ratio
|
||||
// all mrope sections carry it: exact for text, approximate for images
|
||||
for (int64_t sec = 0; sec < 4; ++sec) {
|
||||
for (int64_t s = 0; s < n_ns; ++s) {
|
||||
for (int64_t b = 0; b < n_blocks; ++b) {
|
||||
dst_blk_pos[sec*(n_blocks*n_ns) + s*n_blocks + b] = (int32_t) (b*r);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// one pass per stream: cell j is a different token in each, so no mapping is shared
|
||||
std::vector<int32_t> blk_of(n_kv);
|
||||
std::vector<int32_t> filled(n_blocks);
|
||||
|
||||
for (int64_t s = 0; s < n_ns; ++s) {
|
||||
// ubatch index s*n_tps belongs to this stream; ask which cells array it uses
|
||||
const llama_seq_id seq_of_stream = ubatch->seq_id[s*n_tps][0];
|
||||
const auto & cells = mem->get_mem_idx()->get_cells(seq_of_stream);
|
||||
|
||||
int32_t * cur_cell_blk = dst_cell_blk + s*n_kv;
|
||||
int32_t * cur_blk_cells = dst_blk_cells + s*(r*n_blocks);
|
||||
|
||||
// an incomplete block cannot be pooled; the bias below forces those tail cells in
|
||||
// -1 means no usable block, and block 0 only keeps the gather in range
|
||||
std::fill(blk_of.begin(), blk_of.end(), -1);
|
||||
std::fill(filled.begin(), filled.end(), 0);
|
||||
std::fill(cur_blk_cells, cur_blk_cells + r*n_blocks, 0);
|
||||
|
||||
// a cell no block covers needs its own -inf, which a per-block bias cannot carry
|
||||
// every cache path keeps the position below the cell window, so this stays false
|
||||
bool oor = false;
|
||||
|
||||
for (int64_t j = 0; j < n_kv; ++j) {
|
||||
if (cells.is_empty(j)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const llama_pos p = cells.pos_get(j);
|
||||
const int64_t b = p/r;
|
||||
|
||||
if (b >= n_blocks) {
|
||||
oor = true;
|
||||
continue;
|
||||
}
|
||||
|
||||
blk_of[j] = (int32_t) b;
|
||||
cur_blk_cells[b*r + (p%r)] = (int32_t) j;
|
||||
filled[b]++;
|
||||
}
|
||||
|
||||
GGML_ASSERT((!blk_bias || !oor) && "qsa: cell position runs past the cell window");
|
||||
|
||||
// per-block mode keeps an unpooled cell's real block, so the block's own -inf reaches it
|
||||
// per-cell mode carries that -inf itself and only needs the gather in range
|
||||
for (int64_t j = 0; j < n_kv; ++j) {
|
||||
if (blk_of[j] >= 0 && filled[blk_of[j]] < r && !blk_bias) {
|
||||
blk_of[j] = -1;
|
||||
}
|
||||
cur_cell_blk[j] = blk_of[j] < 0 ? 0 : blk_of[j];
|
||||
}
|
||||
|
||||
for (int64_t ii = 0; ii < n_tps; ++ii) {
|
||||
const int64_t i = s*n_tps + ii;
|
||||
const llama_seq_id seq_id = ubatch->seq_id[i][0];
|
||||
const llama_pos q = ubatch->pos[i];
|
||||
|
||||
// the tail is an incomplete block and is always visible, as in the reference
|
||||
const llama_pos tail_start = (q + 1)/r*r;
|
||||
|
||||
if (blk_bias) {
|
||||
// a block sits wholly inside or outside the tail, so one value covers it
|
||||
// the caller adds the attention mask, which drops empty, foreign and future cells
|
||||
float * cur_blk_bias = dst_bias + i*n_blocks;
|
||||
|
||||
for (int64_t b = 0; b < n_blocks; ++b) {
|
||||
// finite, so it can never meet a -inf and produce a nan
|
||||
cur_blk_bias[b] = b*r >= tail_start ? 1e9f : (filled[b] < r ? -INFINITY : 0.0f);
|
||||
}
|
||||
|
||||
continue;
|
||||
}
|
||||
|
||||
float * cur_bias = dst_bias + i*n_kv;
|
||||
|
||||
for (int64_t j = 0; j < n_kv; ++j) {
|
||||
float v = -INFINITY;
|
||||
|
||||
if (!cells.is_empty(j) && cells.seq_has(j, seq_id) && cells.pos_get(j) <= q) {
|
||||
// finite, so it can never meet a -inf and produce a nan
|
||||
v = cells.pos_get(j) >= tail_start ? 1e9f : (blk_of[j] < 0 ? -INFINITY : 0.0f);
|
||||
}
|
||||
|
||||
cur_bias[j] = v;
|
||||
}
|
||||
}
|
||||
}
|
||||
mem->set_input_qsa(block_cells, block_pos, block_mask, selected,
|
||||
kq_mask, ubatch, ratio, block_topk);
|
||||
}
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
#include "llama-memory-hybrid.h"
|
||||
|
||||
#include <array>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
||||
@@ -75,7 +77,20 @@ public:
|
||||
|
||||
llama_kv_cache * get_mem_idx() const; // nullptr when the model carries no indexer
|
||||
|
||||
void set_input_qsa(ggml_tensor * block_cells, ggml_tensor * block_pos,
|
||||
ggml_tensor * block_mask, ggml_tensor * selected,
|
||||
const ggml_tensor * kq_mask, const llama_ubatch * ubatch,
|
||||
uint32_t ratio, uint32_t block_topk) const;
|
||||
|
||||
void commit_qsa_tokens(const llama_ubatch & ubatch);
|
||||
|
||||
private:
|
||||
struct qsa_token {
|
||||
std::array<llama_pos, 4> pos;
|
||||
};
|
||||
|
||||
using qsa_history = std::vector<qsa_token>;
|
||||
|
||||
// forget seq_id (all of it if seq_id < 0) in every cache at once, so a failed restore cannot leave the caches out of step
|
||||
// seq_id < 0 drops the whole context, as the caches themselves do on a failed restore
|
||||
void state_drop(llama_seq_id seq_id);
|
||||
@@ -85,6 +100,8 @@ private:
|
||||
llama_hparams hparams_idx;
|
||||
|
||||
const std::unique_ptr<llama_kv_cache> mem_idx;
|
||||
|
||||
std::map<llama_seq_id, qsa_history> qsa_histories;
|
||||
};
|
||||
|
||||
class llama_memory_hybrid_idx_context : public llama_memory_hybrid_context {
|
||||
@@ -123,26 +140,20 @@ public:
|
||||
// llama_memory_hybrid_idx_context specific API
|
||||
//
|
||||
|
||||
// nullptr with no indexer, and for the update context, which builds no sparse graph
|
||||
// nullptr with no indexer
|
||||
const llama_kv_cache_context * get_idx() const;
|
||||
|
||||
// streams in the current slot info, the `ns` of get_k/get_v; 1 if unified
|
||||
uint32_t get_n_stream() const;
|
||||
|
||||
// block-compressed sparse attention (qwen4exp QSA) over the cells of the indexer cache.
|
||||
// Blocks cut the position line, not the cell array, so no caller assumes a contiguous layout:
|
||||
// cell_blk I32 [n_kv, ns] block each cell belongs to
|
||||
// blk_cells I32 [ratio*n_blocks, ns] cells making up each block
|
||||
// blk_pos I32 [4*n_blocks*ns] mrope position rows of each block's first token
|
||||
// bias F32 [n_kv, n_tokens/ns, ns] -inf where invisible, large where always visible
|
||||
// blk_bias asks for the bias per block instead: [n_blocks, n_tokens/ns, ns]
|
||||
// the caller then adds the attention mask, the only part of the bias that varies within a block
|
||||
void set_input_qsa(ggml_tensor * cell_blk, ggml_tensor * blk_cells, ggml_tensor * blk_pos,
|
||||
ggml_tensor * bias, const llama_ubatch * ubatch, uint32_t ratio,
|
||||
bool blk_bias) const;
|
||||
// QSA blocks follow each sequence's token order, not physical cells or scalar positions.
|
||||
void set_input_qsa(ggml_tensor * block_cells, ggml_tensor * block_pos,
|
||||
ggml_tensor * block_mask, ggml_tensor * selected,
|
||||
const ggml_tensor * kq_mask, const llama_ubatch * ubatch,
|
||||
uint32_t ratio, uint32_t block_topk) const;
|
||||
|
||||
private:
|
||||
const llama_memory_hybrid_idx * mem = nullptr;
|
||||
llama_memory_hybrid_idx * mem = nullptr;
|
||||
|
||||
// streams per ubatch, read from the slot infos before ctx_idx takes them
|
||||
// declared first, so it is initialised while sinfos_idx is still intact
|
||||
@@ -151,6 +162,8 @@ private:
|
||||
// null unless the model has an indexer and this is a batch or full context
|
||||
const llama_memory_context_ptr ctx_idx;
|
||||
|
||||
const bool has_ubatches = false;
|
||||
|
||||
// mirrors the base class's ubatch cursor, which is private there
|
||||
size_t i_cur = 0;
|
||||
};
|
||||
|
||||
+2
-12
@@ -2310,22 +2310,12 @@ struct llama_model_qwen4exp : public llama_model_base {
|
||||
int * sections,
|
||||
int il);
|
||||
|
||||
// dense self-attention restricted to the cells that top_k names
|
||||
ggml_tensor * build_attn_qsa(
|
||||
llm_graph_input_attn_kv * inp,
|
||||
ggml_tensor * q_cur,
|
||||
ggml_tensor * k_cur,
|
||||
ggml_tensor * v_cur,
|
||||
ggml_tensor * top_k,
|
||||
float kq_scale,
|
||||
int il);
|
||||
|
||||
// the QSA cache layout inputs do not depend on the layer, only on its compress ratio,
|
||||
// so the layers sharing a ratio share one input set
|
||||
std::map<uint32_t, llm_graph_input_qsa *> qsa_inps;
|
||||
|
||||
// QSA: token indices this layer's queries may attend to, or nullptr for dense
|
||||
ggml_tensor * build_qsa_top_k(
|
||||
// QSA mask for this layer, or nullptr for dense attention
|
||||
ggml_tensor * build_qsa_mask(
|
||||
const llama_memory_hybrid_idx_context * mctx_hyb,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * inp_pos,
|
||||
|
||||
+195
-219
@@ -18,21 +18,29 @@ void llama_model_qwen4exp::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
||||
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
||||
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
|
||||
GGML_ASSERT(hparams.ssm_d_conv > 0 && hparams.ssm_d_inner > 0 && hparams.ssm_d_state > 0 &&
|
||||
hparams.ssm_dt_rank > 0 && hparams.ssm_n_group > 0);
|
||||
if (hparams.ssm_d_conv == 0 || hparams.ssm_d_inner == 0 || hparams.ssm_d_state == 0 ||
|
||||
hparams.ssm_dt_rank == 0 || hparams.ssm_n_group == 0 ||
|
||||
hparams.ssm_dt_rank % hparams.ssm_n_group != 0 ||
|
||||
(uint64_t) hparams.ssm_d_state * hparams.ssm_dt_rank != hparams.ssm_d_inner) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp gated delta net dimensions");
|
||||
}
|
||||
|
||||
// HC; low_rank is qwen4exp-specific, DeepSeek-V4 leaves it absent (full rank)
|
||||
ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
|
||||
ml.get_key(LLM_KV_HYPER_CONNECTION_LOW_RANK, hparams.hc_low_rank);
|
||||
GGML_ASSERT(hparams.dsv4_hc_mult > 0 && hparams.hc_low_rank > 0);
|
||||
if (hparams.n_embd == 0 || hparams.dsv4_hc_mult <= 1 || hparams.hc_low_rank == 0 ||
|
||||
hparams.dsv4_hc_mult > UINT32_MAX/hparams.n_embd) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp hyper-connection dimensions");
|
||||
}
|
||||
hparams.n_embd_out_impl = hparams.dsv4_hc_mult * hparams.n_embd;
|
||||
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
|
||||
GGML_ASSERT(hparams.indexer_n_head > 0
|
||||
&& hparams.indexer_head_size > 0
|
||||
&& hparams.indexer_top_k > 0);
|
||||
if (hparams.indexer_n_head == 0 || hparams.indexer_head_size == 0 || hparams.indexer_top_k == 0 ||
|
||||
hparams.n_rot_full > hparams.indexer_head_size) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp sparse-attention dimensions");
|
||||
}
|
||||
ml.get_key_or_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, hparams.n_layer_all, false);
|
||||
|
||||
// PLE n-gram hash embeddings; if the key group is absent every field stays zero
|
||||
@@ -44,9 +52,11 @@ void llama_model_qwen4exp::load_arch_hparams(llama_model_loader & ml) {
|
||||
if (n_ple > 0) {
|
||||
std::vector<uint32_t> ple_layers;
|
||||
ml.get_arr(LLM_KV_PLE_LAYERS, ple_layers);
|
||||
GGML_ASSERT(n_ple == 1 && "qwen4exp supports only one PLE layer");
|
||||
if (n_ple != 1 || ple_layers.size() != n_ple) {
|
||||
throw std::runtime_error("Qwen4-Exp supports exactly one PLE layer");
|
||||
}
|
||||
for (uint32_t il : ple_layers) {
|
||||
if (il >= hparams.n_layer_all) {
|
||||
if (il >= hparams.n_layer()) {
|
||||
throw std::runtime_error(format("PLE layer %u is out of range", il));
|
||||
}
|
||||
hparams.is_ple_impl.set(il);
|
||||
@@ -59,15 +69,30 @@ void llama_model_qwen4exp::load_arch_hparams(llama_model_loader & ml) {
|
||||
// optional: files written before this key fall back to the EOS token
|
||||
ml.get_key(LLM_KV_PLE_IMAGE_TOKEN_ID, hparams.ple_image_token_id, false);
|
||||
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
|
||||
GGML_ASSERT(hparams.ple_conv_kernel > 0 && hparams.n_embd_per_layer > 0);
|
||||
if (hparams.ple_conv_kernel == 0 || hparams.n_embd_per_layer == 0) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp PLE dimensions");
|
||||
}
|
||||
|
||||
hparams.ple_n_heads = (hparams.ple_ngram_size - 1) * hparams.ple_heads_per_ngram;
|
||||
hparams.ple_head_dim = hparams.n_embd_per_layer;
|
||||
if (hparams.ple_ngram_size < 2 || hparams.ple_ngram_size > LLAMA_MAX_PLE_NGRAM) {
|
||||
throw std::runtime_error(format("PLE n-gram size %u is out of range", hparams.ple_ngram_size));
|
||||
}
|
||||
if (hparams.ple_n_heads == 0 || hparams.ple_n_heads > LLAMA_MAX_PLE_HEADS) {
|
||||
throw std::runtime_error(format("PLE head count %u is out of range", hparams.ple_n_heads));
|
||||
|
||||
const uint64_t ple_n_heads = (uint64_t) (hparams.ple_ngram_size - 1) * hparams.ple_heads_per_ngram;
|
||||
hparams.ple_head_dim = hparams.n_embd_per_layer;
|
||||
if (ple_n_heads == 0 || ple_n_heads > LLAMA_MAX_PLE_HEADS) {
|
||||
throw std::runtime_error(format("PLE head count %" PRIu64 " is out of range", ple_n_heads));
|
||||
}
|
||||
hparams.ple_n_heads = (uint32_t) ple_n_heads;
|
||||
|
||||
uint32_t n_multipliers = 0;
|
||||
uint32_t n_offsets = 0;
|
||||
uint32_t n_vocab_sizes = 0;
|
||||
ml.get_arr_n(LLM_KV_PLE_LAYER_MULTIPLIERS, n_multipliers);
|
||||
ml.get_arr_n(LLM_KV_PLE_HEAD_OFFSETS, n_offsets);
|
||||
ml.get_arr_n(LLM_KV_PLE_HEAD_VOCAB_SIZES, n_vocab_sizes);
|
||||
if (n_multipliers != hparams.ple_ngram_size ||
|
||||
n_offsets != hparams.ple_n_heads || n_vocab_sizes != hparams.ple_n_heads) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp PLE metadata lengths");
|
||||
}
|
||||
|
||||
ml.get_arr(LLM_KV_PLE_LAYER_MULTIPLIERS, hparams.ple_layer_multipliers);
|
||||
@@ -93,12 +118,28 @@ void llama_model_qwen4exp::load_arch_hparams(llama_model_loader & ml) {
|
||||
if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
|
||||
uint32_t full_attn_interval = 4;
|
||||
ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
|
||||
GGML_ASSERT(full_attn_interval > 0);
|
||||
if (full_attn_interval == 0) {
|
||||
throw std::runtime_error("invalid Qwen4-Exp full-attention interval");
|
||||
}
|
||||
for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
|
||||
hparams.is_recr_impl[i] = (i < hparams.n_layer()) && ((i + 1) % full_attn_interval != 0);
|
||||
}
|
||||
}
|
||||
|
||||
for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
|
||||
const uint32_t ratio = hparams.dsv4_compress_ratios[il];
|
||||
if (hparams.is_recr(il)) {
|
||||
if (ratio != 0) {
|
||||
throw std::runtime_error(format("Qwen4-Exp recurrent layer %u has a QSA compression ratio", il));
|
||||
}
|
||||
} else if (ratio == 0 || hparams.indexer_top_k % ratio != 0) {
|
||||
throw std::runtime_error(format("invalid Qwen4-Exp QSA compression ratio %u at layer %u", ratio, il));
|
||||
}
|
||||
if (hparams.is_ple(il) && !hparams.is_recr(il)) {
|
||||
throw std::runtime_error(format("Qwen4-Exp PLE layer %u is not recurrent", il));
|
||||
}
|
||||
}
|
||||
|
||||
switch (hparams.n_layer()) {
|
||||
case 48: type = LLM_TYPE_A3B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
@@ -127,8 +168,15 @@ void llama_model_qwen4exp::load_arch_tensors(llama_model_loader & ml) {
|
||||
// flat [ple_head_dim, n_rows] gather target; n_rows is padded, so read it back
|
||||
if (hparams.ple_n_heads > 0) {
|
||||
const std::string ple_name = tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight").str();
|
||||
const auto & ple_w = ml.require_weight(ple_name.c_str());
|
||||
const int64_t ple_rows = ple_w.tensor->ne[1];
|
||||
const auto * ple_w = ml.get_weight(ple_name.c_str());
|
||||
int64_t ple_rows = 0;
|
||||
for (uint32_t h = 0; h < hparams.ple_n_heads; ++h) {
|
||||
ple_rows = std::max<int64_t>(ple_rows,
|
||||
(int64_t) hparams.ple_head_offsets[h] + hparams.ple_head_vocab_sizes[h]);
|
||||
}
|
||||
if (ple_w) {
|
||||
ple_rows = ple_w->tensor->ne[1];
|
||||
}
|
||||
|
||||
// sanity check
|
||||
for (uint32_t h = 0; h < hparams.ple_n_heads; ++h) {
|
||||
@@ -190,8 +238,9 @@ void llama_model_qwen4exp::load_arch_tensors(llama_model_loader & ml) {
|
||||
}
|
||||
|
||||
if (hparams.is_ple(il)) {
|
||||
layer.ple_key = create_tensor(tn(LLM_TENSOR_PLE_KEY, "weight", il), { n_embd, hc_dim }, 0);
|
||||
layer.ple_value = create_tensor(tn(LLM_TENSOR_PLE_VALUE, "weight", il), { n_embd, n_embd }, 0);
|
||||
const int64_t ple_dim = (int64_t) hparams.ple_head_dim * hparams.ple_n_heads;
|
||||
layer.ple_key = create_tensor(tn(LLM_TENSOR_PLE_KEY, "weight", il), { ple_dim, hc_dim }, 0);
|
||||
layer.ple_value = create_tensor(tn(LLM_TENSOR_PLE_VALUE, "weight", il), { ple_dim, n_embd }, 0);
|
||||
layer.ple_norm_key = create_tensor(tn(LLM_TENSOR_PLE_NORM_KEY, "weight", il), { hc_dim }, 0);
|
||||
layer.ple_norm_query = create_tensor(tn(LLM_TENSOR_PLE_NORM_QUERY, "weight", il), { hc_dim }, 0);
|
||||
layer.ple_norm_conv = create_tensor(tn(LLM_TENSOR_PLE_NORM_CONV, "weight", il), { hc_dim }, 0);
|
||||
@@ -416,13 +465,17 @@ ggml_tensor * llama_model_qwen4exp::graph::build_norm_gated(
|
||||
// one mean-pooled indexer key scores each block; set_input resolves the cache layout
|
||||
class llama_model_qwen4exp::llm_graph_input_qsa : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_qsa(const llama_memory_hybrid_idx_context * mctx, uint32_t ratio, bool blk_bias) :
|
||||
mctx(mctx), ratio(ratio), blk_bias(blk_bias) {}
|
||||
llm_graph_input_qsa(
|
||||
const llama_memory_hybrid_idx_context * mctx,
|
||||
ggml_tensor * kq_mask,
|
||||
uint32_t ratio,
|
||||
uint32_t block_topk) :
|
||||
mctx(mctx), kq_mask(kq_mask), ratio(ratio), block_topk(block_topk) {}
|
||||
virtual ~llm_graph_input_qsa() = default;
|
||||
|
||||
void set_input(const llama_ubatch * ubatch) override {
|
||||
mctx->get_idx()->set_input_k_idxs(k_idxs, ubatch);
|
||||
mctx->set_input_qsa(cell_blk, blk_cells, blk_pos, bias, ubatch, ratio, blk_bias);
|
||||
mctx->set_input_qsa(block_cells, block_pos, block_mask, selected,
|
||||
kq_mask, ubatch, ratio, block_topk);
|
||||
}
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override {
|
||||
@@ -434,39 +487,33 @@ public:
|
||||
}
|
||||
|
||||
const int64_t n_kv = idx->get_n_kv();
|
||||
const int64_t n_stream = mctx->get_n_stream();
|
||||
const int64_t n_blocks = (n_kv + ratio - 1)/ratio;
|
||||
const int64_t n_blocks = n_kv/ratio;
|
||||
|
||||
bool res = true;
|
||||
|
||||
res &= params.ubatch.n_tokens % n_stream == 0;
|
||||
|
||||
res &= k_idxs->ne[0] == params.ubatch.n_tokens;
|
||||
res &= cell_blk->ne[0] == n_kv;
|
||||
res &= cell_blk->ne[1] == n_stream;
|
||||
res &= blk_cells->ne[0] == (int64_t) ratio*n_blocks;
|
||||
res &= blk_pos->ne[0] == 4*n_blocks*n_stream;
|
||||
res &= bias->ne[0] == (blk_bias ? n_blocks : n_kv);
|
||||
res &= bias->ne[1] == params.ubatch.n_tokens/n_stream;
|
||||
res &= n_kv > (int64_t) block_topk*ratio + ratio - 1;
|
||||
res &= block_cells->ne[1] == n_blocks;
|
||||
res &= block_cells->ne[2] == params.ubatch.n_tokens;
|
||||
res &= block_pos->ne[2] == params.ubatch.n_tokens;
|
||||
res &= block_mask->ne[1] == params.ubatch.n_tokens;
|
||||
res &= selected->ne[0] == n_kv;
|
||||
res &= selected->ne[1] == params.ubatch.n_tokens;
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
// per stream: a cell index names a different token in each stream
|
||||
ggml_tensor * k_idxs = nullptr; // I32 [n_tokens]
|
||||
ggml_tensor * cell_blk = nullptr; // I32 [n_kv, n_stream]
|
||||
ggml_tensor * blk_cells = nullptr; // I32 [ratio*n_blocks, n_stream]
|
||||
ggml_tensor * blk_pos = nullptr; // I32 [4*n_blocks*n_stream]
|
||||
ggml_tensor * bias = nullptr; // F32 [n_blocks or n_kv, n_tokens/n_stream, n_stream]
|
||||
ggml_tensor * block_cells = nullptr;
|
||||
ggml_tensor * block_pos = nullptr;
|
||||
ggml_tensor * block_mask = nullptr;
|
||||
ggml_tensor * selected = nullptr;
|
||||
|
||||
const llama_memory_hybrid_idx_context * mctx;
|
||||
ggml_tensor * kq_mask;
|
||||
const uint32_t ratio;
|
||||
|
||||
// the per-cell half of the bias is the attention mask, so only the per-block half is uploaded
|
||||
const bool blk_bias;
|
||||
const uint32_t block_topk;
|
||||
};
|
||||
|
||||
ggml_tensor * llama_model_qwen4exp::graph::build_qsa_top_k(
|
||||
ggml_tensor * llama_model_qwen4exp::graph::build_qsa_mask(
|
||||
const llama_memory_hybrid_idx_context * mctx_hyb,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * inp_pos,
|
||||
@@ -482,21 +529,13 @@ ggml_tensor * llama_model_qwen4exp::graph::build_qsa_top_k(
|
||||
|
||||
GGML_ASSERT(r > 0);
|
||||
|
||||
const int64_t n_blocks = (n_kv + r - 1)/r;
|
||||
const int64_t n_blocks = n_kv/r;
|
||||
const int64_t block_topk = hparams.indexer_top_k/r;
|
||||
|
||||
// build_attn_qsa and the KQ mask need the tokens to divide evenly across the streams
|
||||
const int64_t n_stream = mctx_hyb->get_n_stream();
|
||||
GGML_ASSERT(n_tokens % n_stream == 0);
|
||||
const int64_t n_tps = n_tokens/n_stream;
|
||||
|
||||
// only the "which block is visible" half of the bias varies per block
|
||||
// the rest is the visible/not test the attention mask already carries, so upload the per-block half only: 1/ratio of the cells
|
||||
// alibi writes distances instead of a mask and non-causal keeps future cells, so both opt out
|
||||
// the mask also holds an mrope rule for the query's own position, but only 2d image positions can differ there
|
||||
const bool blk_bias = kq_mask != nullptr &&
|
||||
kq_mask->ne[0] == n_kv && kq_mask->ne[1] == n_tps && kq_mask->ne[3] == n_stream &&
|
||||
cparams.causal_attn && !hparams.use_alibi;
|
||||
|
||||
// nothing above depends on the layer, so the layers sharing a ratio share one input set
|
||||
llm_graph_input_qsa * inp = nullptr;
|
||||
|
||||
@@ -504,59 +543,29 @@ ggml_tensor * llama_model_qwen4exp::graph::build_qsa_top_k(
|
||||
if (it != qsa_inps.end()) {
|
||||
inp = it->second;
|
||||
} else {
|
||||
auto qsa = std::make_unique<llm_graph_input_qsa>(mctx_hyb, (uint32_t) r, blk_bias);
|
||||
auto qsa = std::make_unique<llm_graph_input_qsa>(
|
||||
mctx_hyb, kq_mask, (uint32_t) r, (uint32_t) block_topk);
|
||||
|
||||
qsa->k_idxs = mctx_idx->build_input_k_idxs(ctx0, ubatch);
|
||||
qsa->cell_blk = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_stream);
|
||||
qsa->blk_cells = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, r*n_blocks, n_stream);
|
||||
qsa->blk_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, 4*n_blocks*n_stream);
|
||||
qsa->bias = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, blk_bias ? n_blocks : n_kv, n_tps, n_stream);
|
||||
qsa->block_cells = ggml_new_tensor_3d(ctx0, GGML_TYPE_I32, r, n_blocks, n_tokens);
|
||||
qsa->block_pos = ggml_new_tensor_3d(ctx0, GGML_TYPE_I32, n_blocks, 4, n_tokens);
|
||||
qsa->block_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_blocks, n_tokens);
|
||||
qsa->selected = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
|
||||
|
||||
ggml_set_input(qsa->cell_blk);
|
||||
ggml_set_input(qsa->blk_cells);
|
||||
ggml_set_input(qsa->blk_pos);
|
||||
ggml_set_input(qsa->bias);
|
||||
ggml_set_input(qsa->block_cells);
|
||||
ggml_set_input(qsa->block_pos);
|
||||
ggml_set_input(qsa->block_mask);
|
||||
ggml_set_input(qsa->selected);
|
||||
|
||||
inp = qsa.get();
|
||||
res->add_input(std::move(qsa));
|
||||
qsa_inps.emplace((uint32_t) r, inp);
|
||||
}
|
||||
|
||||
// cached indexer keys are raw: pooling precedes norm and rotation, so apply neither
|
||||
ggml_tensor * k_raw = build_lora_mm(model.layers[il].index_k_proj, cur);
|
||||
k_raw = ggml_reshape_3d(ctx0, k_raw, idx_dim, 1, n_tokens);
|
||||
cb(k_raw, "indexer_k_raw", il);
|
||||
|
||||
ggml_build_forward_expand(gf, mctx_idx->cpy_k(ctx0, k_raw, inp->k_idxs, il));
|
||||
kq_mask = inp->kq_mask;
|
||||
|
||||
// one key head, so rows are contiguous. get_k gives [idx_dim, n_head_kv, n_kv, n_stream].
|
||||
ggml_tensor * k_all = mctx_idx->get_k(ctx0, il);
|
||||
k_all = ggml_view_3d(ctx0, k_all, idx_dim, n_kv, n_stream, k_all->nb[2], k_all->nb[3], 0);
|
||||
|
||||
// gathers per stream: blk_cells row s indexes stream s's own cells
|
||||
ggml_tensor * members = ggml_get_rows(ctx0, k_all, inp->blk_cells);
|
||||
members = ggml_reshape_4d(ctx0, members, idx_dim, r, n_blocks, n_stream);
|
||||
|
||||
// mean over the block members; r is small, so summing slices beats a transpose plus sum_rows
|
||||
ggml_tensor * pooled = nullptr;
|
||||
for (int64_t i = 0; i < r; ++i) {
|
||||
ggml_tensor * slice = ggml_cont(ctx0,
|
||||
ggml_view_3d(ctx0, members, idx_dim, n_blocks, n_stream,
|
||||
members->nb[2], members->nb[3], i*members->nb[1]));
|
||||
pooled = pooled ? ggml_add(ctx0, pooled, slice) : slice;
|
||||
}
|
||||
pooled = ggml_scale(ctx0, pooled, 1.0f/(float) r);
|
||||
cb(pooled, "indexer_k_pooled", il);
|
||||
|
||||
// rope wants [n_dims, n_head, n_tokens]: lay every stream's blocks flat, split after.
|
||||
pooled = ggml_reshape_3d(ctx0, pooled, idx_dim, 1, n_blocks*n_stream);
|
||||
pooled = build_norm(pooled, model.layers[il].index_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
pooled = ggml_rope_multi(ctx0, pooled, inp->blk_pos, nullptr,
|
||||
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
pooled = ggml_reshape_3d(ctx0, pooled, idx_dim, n_blocks, n_stream);
|
||||
cb(pooled, "indexer_k", il);
|
||||
|
||||
ggml_tensor * q = build_lora_mm(model.layers[il].index_q_proj, cur);
|
||||
q = ggml_reshape_3d(ctx0, q, idx_dim, n_idx_h, n_tokens);
|
||||
q = build_norm(q, model.layers[il].index_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
@@ -565,128 +574,86 @@ ggml_tensor * llama_model_qwen4exp::graph::build_qsa_top_k(
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q, "indexer_q", il);
|
||||
|
||||
// rectify each head dot product before the sum, as in the DeepSeek lightning indexer
|
||||
// mul_mat matches ne[2], so the queries of stream s only meet the blocks of stream s
|
||||
ggml_tensor * score = ggml_mul_mat(ctx0, pooled,
|
||||
ggml_reshape_3d(ctx0, ggml_cont(ctx0, q), idx_dim, n_idx_h*n_tps, n_stream));
|
||||
score = ggml_reshape_4d(ctx0, score, n_blocks, n_idx_h, n_tps, n_stream);
|
||||
score = ggml_relu(ctx0, score);
|
||||
score = ggml_cont(ctx0, ggml_permute(ctx0, score, 1, 0, 2, 3));
|
||||
score = ggml_sum_rows(ctx0, score);
|
||||
score = ggml_reshape_3d(ctx0, score, n_blocks, n_tps, n_stream);
|
||||
cb(score, "indexer_score", il);
|
||||
const ggml_type activation_type = mctx_idx->type_k();
|
||||
std::vector<ggml_tensor *> selected_streams;
|
||||
selected_streams.reserve(n_stream);
|
||||
|
||||
// one value per block, so it is cheaper to bias here than after the cells are expanded
|
||||
if (blk_bias) {
|
||||
score = ggml_add(ctx0, score, inp->bias);
|
||||
for (int64_t is = 0; is < n_stream; ++is) {
|
||||
ggml_tensor * cache = ggml_view_2d(ctx0, k_all, idx_dim, n_kv,
|
||||
k_all->nb[1], is*k_all->nb[2]);
|
||||
ggml_tensor * block_cells = ggml_view_3d(ctx0, inp->block_cells, r, n_blocks, n_tps,
|
||||
inp->block_cells->nb[1], inp->block_cells->nb[2], is*n_tps*inp->block_cells->nb[2]);
|
||||
ggml_tensor * block_keys = ggml_get_rows(ctx0, cache,
|
||||
ggml_reshape_1d(ctx0, block_cells, r*n_blocks*n_tps));
|
||||
block_keys = ggml_reshape_4d(ctx0, block_keys, idx_dim, r, n_blocks, n_tps);
|
||||
block_keys = ggml_cont(ctx0, ggml_transpose(ctx0, block_keys));
|
||||
block_keys = ggml_mean(ctx0, block_keys);
|
||||
block_keys = ggml_cont(ctx0, ggml_transpose(ctx0, block_keys));
|
||||
block_keys = ggml_reshape_3d(ctx0, block_keys, idx_dim, 1, n_blocks*n_tps);
|
||||
|
||||
if (block_keys->type != activation_type) {
|
||||
block_keys = ggml_cast(ctx0, block_keys, activation_type);
|
||||
block_keys = ggml_cast(ctx0, block_keys, GGML_TYPE_F32);
|
||||
}
|
||||
block_keys = build_norm(block_keys, model.layers[il].index_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
|
||||
ggml_tensor * block_pos = ggml_view_3d(ctx0, inp->block_pos, n_blocks, 4, n_tps,
|
||||
inp->block_pos->nb[1], inp->block_pos->nb[2], is*n_tps*inp->block_pos->nb[2]);
|
||||
block_keys = ggml_rope_multi(ctx0, block_keys,
|
||||
ggml_reshape_1d(ctx0, block_pos, n_blocks*4*n_tps), nullptr,
|
||||
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(block_keys, "indexer_k", il);
|
||||
|
||||
block_keys = ggml_reshape_4d(ctx0, block_keys, idx_dim, n_blocks, 1, n_tps);
|
||||
ggml_tensor * query = ggml_view_3d(ctx0, q, idx_dim, n_idx_h, n_tps,
|
||||
q->nb[1], q->nb[2], is*n_tps*q->nb[2]);
|
||||
query = ggml_reshape_4d(ctx0, query, idx_dim, n_idx_h, 1, n_tps);
|
||||
|
||||
ggml_tensor * scores = ggml_mul_mat(ctx0, block_keys, query);
|
||||
ggml_mul_mat_set_prec(scores, GGML_PREC_F32);
|
||||
scores = ggml_relu(ctx0, scores);
|
||||
scores = ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_permute(ctx0, scores, 1, 0, 2, 3)));
|
||||
scores = ggml_scale(ctx0, ggml_reshape_2d(ctx0, scores, n_blocks, n_tps),
|
||||
1.0f/sqrtf((float) idx_dim));
|
||||
|
||||
ggml_tensor * block_mask = ggml_view_2d(ctx0, inp->block_mask, n_blocks, n_tps,
|
||||
inp->block_mask->nb[1], is*n_tps*inp->block_mask->nb[1]);
|
||||
scores = ggml_add(ctx0, scores, block_mask);
|
||||
cb(scores, "indexer_score", il);
|
||||
|
||||
ggml_tensor * top_blocks = ggml_top_k(ctx0, scores, block_topk);
|
||||
ggml_tensor * top_cells = ggml_get_rows(ctx0, block_cells, top_blocks);
|
||||
top_cells = ggml_reshape_2d(ctx0, top_cells, r*block_topk, n_tps);
|
||||
|
||||
ggml_tensor * base_selected = ggml_view_2d(ctx0, inp->selected, n_kv, n_tps,
|
||||
inp->selected->nb[1], is*n_tps*inp->selected->nb[1]);
|
||||
base_selected = ggml_reshape_3d(ctx0, base_selected, 1, n_kv, n_tps);
|
||||
ggml_tensor * selected_top = ggml_fill(ctx0, base_selected, 0.0f);
|
||||
ggml_tensor * ones = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, r*block_topk, n_tps);
|
||||
ones = ggml_fill(ctx0, ones, 1.0f);
|
||||
selected_top = ggml_set_rows(ctx0, selected_top, ones, top_cells);
|
||||
ggml_tensor * selected_stream = ggml_clamp(
|
||||
ctx0, ggml_add(ctx0, base_selected, selected_top), 0.0f, 1.0f);
|
||||
selected_streams.push_back(ggml_reshape_2d(ctx0, selected_stream, n_kv, n_tps));
|
||||
}
|
||||
|
||||
// every token of a block gets the block score; the budget is whole blocks, so top-k cuts on a block boundary
|
||||
ggml_tensor * expanded = ggml_get_rows(ctx0,
|
||||
ggml_cont(ctx0, ggml_permute(ctx0, score, 1, 0, 2, 3)), inp->cell_blk);
|
||||
expanded = ggml_cont(ctx0, ggml_permute(ctx0, expanded, 1, 0, 2, 3));
|
||||
|
||||
if (blk_bias) {
|
||||
// flash attention keeps the mask in f16; the scores are f32
|
||||
ggml_tensor * mask = kq_mask->type == GGML_TYPE_F32 ? kq_mask : ggml_cast(ctx0, kq_mask, GGML_TYPE_F32);
|
||||
expanded = ggml_add(ctx0, expanded, ggml_reshape_3d(ctx0, mask, n_kv, n_tps, n_stream));
|
||||
} else {
|
||||
expanded = ggml_add(ctx0, expanded, inp->bias);
|
||||
ggml_tensor * selected = selected_streams[0];
|
||||
for (int64_t is = 1; is < n_stream; ++is) {
|
||||
selected = ggml_concat(ctx0, selected, selected_streams[is], 1);
|
||||
}
|
||||
cb(expanded, "indexer_score_tokens", il);
|
||||
selected = ggml_scale_bias(ctx0, selected, 1e30f, -1e30f);
|
||||
|
||||
// the reference returns indexer_top_k + compress_ratio - 1: whole blocks plus the tail
|
||||
const int64_t width = std::min<int64_t>(n_kv, (int64_t) hparams.indexer_top_k + r - 1);
|
||||
|
||||
ggml_tensor * top_k = ggml_cont(ctx0, ggml_top_k(ctx0, expanded, width));
|
||||
|
||||
// build_attn_qsa reads [n_top_k, n_batch, 1, n_stream], matching the KQ mask.
|
||||
top_k = ggml_reshape_4d(ctx0, top_k, width, n_tps, 1, n_stream);
|
||||
cb(top_k, "indexer_top_k", il);
|
||||
|
||||
return top_k;
|
||||
}
|
||||
|
||||
// Dense GQA self-attention restricted to the cells that top_k names.
|
||||
// The mask build below copies the MLA sparse path in llm_graph_context::build_attn.
|
||||
ggml_tensor * llama_model_qwen4exp::graph::build_attn_qsa(
|
||||
llm_graph_input_attn_kv * inp,
|
||||
ggml_tensor * q_cur,
|
||||
ggml_tensor * k_cur,
|
||||
ggml_tensor * v_cur,
|
||||
ggml_tensor * top_k,
|
||||
float kq_scale,
|
||||
int il) {
|
||||
// rotate q/k/v before they reach a quantized cache, as the dense path does. the indexer
|
||||
// has already scored with its own query in build_qsa_top_k, so top_k is unaffected.
|
||||
if (inp->self_k_rot) {
|
||||
q_cur = llama_mul_mat_hadamard(ctx0, q_cur, inp->self_k_rot);
|
||||
k_cur = llama_mul_mat_hadamard(ctx0, k_cur, inp->self_k_rot);
|
||||
ggml_tensor * base_mask = ggml_reshape_2d(ctx0, kq_mask, n_kv, n_tokens);
|
||||
if (base_mask->type != GGML_TYPE_F32) {
|
||||
base_mask = ggml_cast(ctx0, base_mask, GGML_TYPE_F32);
|
||||
}
|
||||
|
||||
if (inp->self_v_rot) {
|
||||
v_cur = llama_mul_mat_hadamard(ctx0, v_cur, inp->self_v_rot);
|
||||
ggml_tensor * mask = ggml_add(ctx0, base_mask, selected);
|
||||
if (cparams.flash_attn) {
|
||||
mask = ggml_cast(ctx0, mask, GGML_TYPE_F16);
|
||||
}
|
||||
|
||||
// these nodes are added to the graph together so that they are not reordered
|
||||
// by doing so, the number of splits in the graph is reduced
|
||||
// expand k later to enable rope fusion which directly writes into k-v cache
|
||||
ggml_build_forward_expand(gf, q_cur);
|
||||
ggml_build_forward_expand(gf, v_cur);
|
||||
ggml_build_forward_expand(gf, k_cur);
|
||||
|
||||
const auto * mctx_cur = inp->mctx;
|
||||
|
||||
// store to KV cache
|
||||
{
|
||||
const auto & k_idxs = inp->get_k_idxs();
|
||||
const auto & v_idxs = inp->get_v_idxs();
|
||||
|
||||
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
|
||||
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
|
||||
}
|
||||
|
||||
ggml_tensor * kq_mask = inp->get_kq_mask();
|
||||
|
||||
// prepare new kq mask - starts filled with -INFINITY
|
||||
ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY);
|
||||
|
||||
// reshape KQ mask into tensor with rows of size 1:
|
||||
// [n_kv, n_batch, 1, n_stream] -> [1, n_kv, n_batch, n_stream]
|
||||
kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3], kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0);
|
||||
|
||||
// reshape top_k indices: [n_top_k, n_batch, 1, n_stream] -> [n_top_k, n_batch, n_stream, 1]
|
||||
ggml_tensor * top_k_3d = ggml_view_4d(ctx0, top_k, top_k->ne[0], top_k->ne[1], top_k->ne[3], 1, top_k->nb[1], top_k->nb[2], top_k->ne[3]*top_k->nb[3], 0);
|
||||
|
||||
// prepare zero-filled tensor with rows of size 1: [1, n_top_k, n_batch, n_stream]
|
||||
// this will be our source of zero values for unmasking top k mask elements
|
||||
ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]);
|
||||
zeros = ggml_fill(ctx0, zeros, 0.0f);
|
||||
|
||||
// modify KQ mask by unmasking elements that are in top_k indices
|
||||
// ggml_set_rows([1, n_kv, n_batch, n_stream], [1, n_top_k, n_batch, n_stream], [n_top_k, n_batch, n_stream, 1])
|
||||
ggml_tensor * kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d);
|
||||
|
||||
// reshape to restore the original shape of KQ mask:
|
||||
// [1, n_kv, n_batch, n_stream] -> [n_kv, n_batch, 1, n_stream]
|
||||
kq_mask_top_k = ggml_view_4d(ctx0, kq_mask_top_k, kq_mask_top_k->ne[1], kq_mask_top_k->ne[2], 1, kq_mask_top_k->ne[3], kq_mask_top_k->nb[2], kq_mask_top_k->nb[3], kq_mask_top_k->nb[3], 0);
|
||||
|
||||
// combine with the original kq mask
|
||||
kq_mask_top_k = ggml_add(ctx0, kq_mask_top_k, kq_mask);
|
||||
|
||||
ggml_tensor * q = q_cur;
|
||||
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
|
||||
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
|
||||
|
||||
ggml_tensor * cur = build_attn_mha(q, k, v, nullptr, kq_mask_top_k, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
|
||||
// the rotation is its own inverse, so undo it on the value side of the output
|
||||
if (inp->self_v_rot) {
|
||||
cur = llama_mul_mat_hadamard(ctx0, cur, inp->self_v_rot);
|
||||
}
|
||||
|
||||
return cur;
|
||||
cb(mask, "qsa_mask", il);
|
||||
return mask;
|
||||
}
|
||||
|
||||
ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn(
|
||||
@@ -699,10 +666,23 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn(
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
|
||||
|
||||
// indexer reads the same block input as q/k/v; no cache or no ratio means dense
|
||||
const bool qsa = mctx_hyb->get_idx() != nullptr && hparams.dsv4_compress_ratios[il] > 0;
|
||||
const llama_kv_cache_context * mctx_idx = mctx_hyb->get_idx();
|
||||
if (mctx_idx) {
|
||||
ggml_tensor * index_k = build_lora_mm(model.layers[il].index_k_proj, cur);
|
||||
index_k = ggml_reshape_3d(ctx0, index_k, hparams.indexer_head_size, 1, n_tokens);
|
||||
cb(index_k, "indexer_k_raw", il);
|
||||
ggml_build_forward_expand(gf, mctx_idx->cpy_k(ctx0, index_k, inp->get_k_idxs(), il));
|
||||
}
|
||||
|
||||
ggml_tensor * top_k = qsa ? build_qsa_top_k(mctx_hyb, cur, inp_pos, inp->get_kq_mask(), sections, il) : nullptr;
|
||||
const int64_t ratio = hparams.dsv4_compress_ratios[il];
|
||||
const bool qsa = mctx_idx && ratio > 0 &&
|
||||
mctx_idx->get_n_kv() > (int64_t) hparams.indexer_top_k + ratio - 1;
|
||||
if (qsa) {
|
||||
inp->self_kq_mask_cnv = build_qsa_mask(
|
||||
mctx_hyb, cur, inp_pos, inp->self_kq_mask, sections, il);
|
||||
} else {
|
||||
inp->self_kq_mask_cnv = inp->self_kq_mask;
|
||||
}
|
||||
|
||||
// Qwen3Next uses a single Q projection that outputs query + gate
|
||||
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]
|
||||
@@ -754,13 +734,9 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn(
|
||||
|
||||
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
|
||||
|
||||
if (top_k) {
|
||||
cur = build_attn_qsa(inp, Qcur, Kcur, Vcur, top_k, kq_scale, il);
|
||||
} else {
|
||||
cur = build_attn(inp,
|
||||
nullptr, nullptr, nullptr,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
}
|
||||
cur = build_attn(inp,
|
||||
nullptr, nullptr, nullptr,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_pregate", il);
|
||||
|
||||
ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
|
||||
|
||||
@@ -220,6 +220,16 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
|
||||
FIXTURES_REQUIRED generate-models
|
||||
)
|
||||
|
||||
llama_test(
|
||||
test-recurrent-state-rollback
|
||||
NAME test-recurrent-state-rollback-qwen4exp
|
||||
LABEL main
|
||||
ARGS -m "${MODEL_DIR}/qwen4exp-moe.gguf"
|
||||
)
|
||||
set_tests_properties(test-recurrent-state-rollback-qwen4exp PROPERTIES
|
||||
FIXTURES_REQUIRED generate-models
|
||||
)
|
||||
|
||||
llama_test(
|
||||
test-recurrent-state-rollback
|
||||
NAME test-recurrent-state-rollback-nemotron-h
|
||||
|
||||
@@ -9358,6 +9358,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
// test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 512, 262144, 9216, {1, 1}, {1, 1}));
|
||||
|
||||
// test large experts*tokens
|
||||
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 512, 10, false, 64, 512, 64));
|
||||
for (bool b : {false, true}) {
|
||||
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, b, 32, 1024, 16));
|
||||
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 2, 2, b, 32, 8192, 64));
|
||||
|
||||
@@ -79,7 +79,7 @@ static std::vector<llama_token> get_tokens(const uint32_t n_tokens, const uint32
|
||||
return ret;
|
||||
}
|
||||
|
||||
static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
|
||||
static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe, const bool qwen_ple = false) {
|
||||
gguf_context_ptr ret(gguf_init_empty());
|
||||
llama_model_saver ms(arch, ret.get());
|
||||
const uint32_t n_ctx = 256;
|
||||
@@ -252,8 +252,23 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
|
||||
if (arch == LLM_ARCH_QWEN4EXP) {
|
||||
ms.add_kv(LLM_KV_HYPER_CONNECTION_COUNT, uint32_t(4));
|
||||
ms.add_kv(LLM_KV_HYPER_CONNECTION_LOW_RANK, uint32_t(8));
|
||||
// without this the QSA layers fall back to dense and go uncovered
|
||||
ms.add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, std::vector<uint32_t>(n_layer, 4));
|
||||
std::vector<uint32_t> ratios(n_layer, 0);
|
||||
for (uint32_t il = 1; il < n_layer; il += 2) {
|
||||
ratios[il] = 4;
|
||||
}
|
||||
ms.add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, ratios);
|
||||
|
||||
if (qwen_ple) {
|
||||
ms.add_kv(LLM_KV_PLE_LAYERS, std::vector<uint32_t>({0}));
|
||||
ms.add_kv(LLM_KV_PLE_NGRAM_SIZE, uint32_t(2));
|
||||
ms.add_kv(LLM_KV_PLE_HEADS_PER_NGRAM, uint32_t(1));
|
||||
ms.add_kv(LLM_KV_PLE_CONV_KERNEL, uint32_t(2));
|
||||
ms.add_kv(LLM_KV_PLE_EOS_TOKEN_ID, uint32_t(1));
|
||||
ms.add_kv(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, uint32_t(64));
|
||||
ms.add_kv(LLM_KV_PLE_LAYER_MULTIPLIERS, std::vector<uint64_t>({1, 3}));
|
||||
ms.add_kv(LLM_KV_PLE_HEAD_OFFSETS, std::vector<uint64_t>({0}));
|
||||
ms.add_kv(LLM_KV_PLE_HEAD_VOCAB_SIZES, std::vector<uint64_t>({16}));
|
||||
}
|
||||
}
|
||||
|
||||
// minimax-m3 keeps one indexer head per GQA head; the rest use a fixed 64 to match the fused
|
||||
@@ -347,7 +362,8 @@ static bool silent_model_load_progress(float /*progress*/, void * /*user_data*/)
|
||||
|
||||
static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
|
||||
struct gguf_context * gguf_ctx, FILE * file, const size_t seed, const std::vector<ggml_backend_dev_t> & devs,
|
||||
const llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER, bool encode = false) {
|
||||
const llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER, bool encode = false,
|
||||
ggml_backend_sched_eval_callback cb_eval = nullptr, void * cb_eval_user_data = nullptr) {
|
||||
GGML_ASSERT((gguf_ctx == nullptr) != (file == nullptr));
|
||||
llama_model_params model_params = llama_model_default_params();
|
||||
model_params.progress_callback = silent_model_load_progress;
|
||||
@@ -360,6 +376,8 @@ static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
|
||||
ctx_params.n_ctx = 0;
|
||||
ctx_params.n_threads = 4;
|
||||
ctx_params.n_threads_batch = 4;
|
||||
ctx_params.cb_eval = cb_eval;
|
||||
ctx_params.cb_eval_user_data = cb_eval_user_data;
|
||||
if (!encode) {
|
||||
ctx_params.n_ubatch = 64;
|
||||
}
|
||||
@@ -412,6 +430,54 @@ static std::vector<float> get_logits(
|
||||
return ret;
|
||||
}
|
||||
|
||||
struct qwen4_qsa_mask_check {
|
||||
int64_t n_seen = 0;
|
||||
int64_t n_tokens_seen = 0;
|
||||
bool ok = true;
|
||||
};
|
||||
|
||||
static bool check_qwen4_qsa_mask(ggml_tensor * tensor, bool ask, void * user_data) {
|
||||
if (strncmp(tensor->name, "qsa_mask", 8) != 0) {
|
||||
return false;
|
||||
}
|
||||
if (ask) {
|
||||
return true;
|
||||
}
|
||||
|
||||
auto * check = (qwen4_qsa_mask_check *) user_data;
|
||||
const int64_t n_kv = tensor->ne[0];
|
||||
const int64_t n_tokens = tensor->ne[1];
|
||||
std::vector<float> mask(ggml_nelements(tensor));
|
||||
if (tensor->type == GGML_TYPE_F32) {
|
||||
ggml_backend_tensor_get(tensor, mask.data(), 0, ggml_nbytes(tensor));
|
||||
} else {
|
||||
GGML_ASSERT(tensor->type == GGML_TYPE_F16);
|
||||
std::vector<ggml_fp16_t> mask_f16(ggml_nelements(tensor));
|
||||
ggml_backend_tensor_get(tensor, mask_f16.data(), 0, ggml_nbytes(tensor));
|
||||
for (size_t i = 0; i < mask.size(); ++i) {
|
||||
mask[i] = ggml_fp16_to_fp32(mask_f16[i]);
|
||||
}
|
||||
}
|
||||
|
||||
for (int64_t it = 0; it < n_tokens; ++it) {
|
||||
const int64_t n_visible = check->n_tokens_seen + it + 1;
|
||||
const int64_t n_complete = n_visible/4;
|
||||
const int64_t expected = n_complete <= 2 ? n_visible : 8 + n_visible%4;
|
||||
int64_t actual = 0;
|
||||
for (int64_t ikv = 0; ikv < n_kv; ++ikv) {
|
||||
actual += mask[it*n_kv + ikv] > -1e20f;
|
||||
}
|
||||
if (actual != expected) {
|
||||
fprintf(stderr, "Qwen4 QSA mask row %lld selects %lld tokens, expected %lld\n",
|
||||
(long long) (check->n_tokens_seen + it), (long long) actual, (long long) expected);
|
||||
}
|
||||
check->ok = check->ok && actual == expected;
|
||||
}
|
||||
check->n_tokens_seen += n_tokens;
|
||||
check->n_seen++;
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool moe_mandatory(const llm_arch arch) {
|
||||
switch (arch) {
|
||||
case LLM_ARCH_LLAMA4:
|
||||
@@ -686,6 +752,32 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
|
||||
if (arch == LLM_ARCH_BAILINGMOE3) {
|
||||
GGML_ASSERT(gguf_remove_key(gguf_ctx.get(), "bailingmoe3.kda.safe_gate") >= 0);
|
||||
}
|
||||
if (arch == LLM_ARCH_QWEN4EXP) {
|
||||
qwen4_qsa_mask_check check;
|
||||
auto model_and_ctx = get_model_and_ctx(
|
||||
gguf_ctx.get(), nullptr, seed, {}, LLAMA_SPLIT_MODE_LAYER, false,
|
||||
check_qwen4_qsa_mask, &check);
|
||||
get_logits(model_and_ctx.first.get(), model_and_ctx.second.get(), tokens);
|
||||
GGML_ASSERT(check.ok && check.n_seen > 0);
|
||||
|
||||
gguf_context_ptr gguf_ctx_ple = get_gguf_ctx(arch, moe, true);
|
||||
auto model_and_ctx_ple = get_model_and_ctx(gguf_ctx_ple.get(), nullptr, seed, {});
|
||||
const std::vector<float> logits_ple = get_logits(
|
||||
model_and_ctx_ple.first.get(), model_and_ctx_ple.second.get(), tokens);
|
||||
|
||||
FILE * file_ple = tmpfile();
|
||||
GGML_ASSERT(file_ple);
|
||||
llama_model_saver saver_ple(model_and_ctx_ple.first.get());
|
||||
saver_ple.add_kv_from_model();
|
||||
saver_ple.add_tensors_from_model();
|
||||
saver_ple.save(file_ple);
|
||||
rewind(file_ple);
|
||||
|
||||
auto model_and_ctx_ple_saved = get_model_and_ctx(nullptr, file_ple, seed, {});
|
||||
const std::vector<float> logits_ple_saved = get_logits(
|
||||
model_and_ctx_ple_saved.first.get(), model_and_ctx_ple_saved.second.get(), tokens);
|
||||
GGML_ASSERT(logits_ple == logits_ple_saved);
|
||||
}
|
||||
std::pair<llama_model_ptr, llama_context_ptr> model_and_ctx_cpu;
|
||||
std::vector<float> logits_cpu;
|
||||
for (device_config & dc : dev_configs) {
|
||||
|
||||
Reference in New Issue
Block a user