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hexagon: support for multi-NPU devices (IQ9, IQ10) and fully asynchronous backend (#26501)

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* hexagon: use non-host bufs by default and make the backend fully async

* hex-hb: remove optional hostbuf support and fix async copy

* hex-unary: relax supported unary check

* hex-bufs: use same get_alignment for host bufs

* snapdragon: bump android_platform to 34

* hex-rows: super hacky get/set rows for q8_0

* hex-get-rows: fix q8_0

* hex-get-rows: supprot for f16 and cleanup for q8_0

* hex-get-rows: generic macros and specialized thread funcs

* hex-get-rows: add DMA pipeline, vtcm_layout and kernel params

* hex-set-rows: fix q8_0 support, add dma and tracing

* hex-tests: override nmse threshold for HTP of Q8_0 quants

* hex-fa: add support for Q8_0 with inplace dequantizers

* hex-get-rows: simplify type dispatch

* hex-rows: simplify GET/SET_ROWS DMA pipeline

* hex-async: add events, set/get-tensor-async and rest of the async api support

* hex-repack: use slice instead of expert in repack functions

* hex-cpy: update event/async-cpy logging

* hex-set-rows: optimize smaller tensors

* hex-geglu: fix perf regression with larger tensors

* hex-get-rows: add missing header

* hex-set-rows: add missing header

* hex-bufs: ressurect GGML_HEXAGON_HOSTBUF but disable it by default

* hexagon: do not reject ops with non-heaxon buffers

* hex-get-rows: apply >=32 restriction only for q8_0

* hex-res: bump vtcm acquire timeout to 10 seconds

* hex-bufs: add support for cloning buffers between sessions to speed up tensor copies

* hex-async: rework event recording and batch flushing and integrate with meta backend

* hex-bufs: improved handling of repacked tensors

* hex-repack: handle get_tensor_2d offsets

* hex-dev: add support for devices with multiple NPUs

* hex-sync: add support for sync tokens to synchronize npu devices for async splits

* hex-mmap: cleanup mmap calls and add a retry for robustness

* hex-sync: add failsafe if sync wait gets stuck

* hex-sync: use sync_seq to check for completed events

* hex-sync: rotate tokens for extra robustness

* hex-devs: add supprot for legacy device names for now

* hex-bufs: add support for auto-cloning buffers from diff sessions

* hex-fusion: simplify and optimize htp-opnode fusion handling

* hex-sync: override opnode name so that it shows up in the profiles

* hex-trace: update scripts to handle multiple devices

* hex-sync: bump the size of the opbatch queue and number of sync tokens

* hex-cpy-sync: do not explicitly flush opbatches in cpy_tensor_async and add support for cpy-dma

* hex-sync: add graph-flush threshold to avoid single op batches

* hex-sync: add sync_peer so that we can flush peers we depend on during cross-device ops

* hex-bufs: introduce tensor->extra and shadow_bufs for repacking

* hex-l2: flush tiny tensors inline

* hex-sync: use explicit l2flush for sync tokens

* hex-extra: track weight flags via tensor extra

* hex-fence: rename sync to fence

* hex-repack: proper handling of set-tensor-2d in the shadow_buf

* hex-trace: remove obsolete opstage mask that we used for profiling

* hex-env: remove obsolete use_hmx variable

* hexagon: new unified run.py and build.py and updated docs

* snapdragon: update run script to auto-escapt test-backend-op -p argument

* hex-scripts: fix trailing spaces

* hex-scripts: fix flake8 warnings

* snapdragon: cleanup dst lib/bin dirs before copying new build

* hex-ops: add support for allreduce

* hex-ar: improved allreduce with dma pipeline

* hex-ar: align macros

* hex-ar: consistent use of fence_seq

* hex-ar: add AR_SELECT env var to select ALLREDUCE kernel or fallback

* hex-ar: add proper synchronize handling for ALLREDUCE

* hex-opbatch: looks like we now just rely on backend.synchronise to flush the batches, no need to flush them by threshold

* hex-ar: bump block size to improve dma efficiency

* hex-ar: fused ALLREDUCE+ADD

* hex-ar: cleaner fence buffer management

* hex-ar: futher allreduce tweaking to remove race conditions

* hex-ar: add simple solver and remove non-dma kernels

* hex-ar: add row-broadcast to fuse with bias ADD

* hex-fence: pass seq numbers via op_params

* hex-ar: allow for both entry/exit seq for completing entry wait

* hex-ar: align macros

* hex-ar: do not refetch broadcast row

* hex-fusion: move all fusion into opbatch::add_op for consistency with ALLREDUCE and things

* hex-fusion: fix incorrect MUL_MAT reordering

* hex-mm: make fused 2x and 3x matmuls more generic

* hex-fusion: move tensor fusion tagging to graph_compute

* hexagon: make sure to copy tensor->extra by value

* hex-get-rows: fix offset calc with row-chunking

* hex-repack: get_tensor_2d fixes for non-zero offsets

* snapdragon: make profile/trace scripts more robust and donot mix stdout/stderr by default

* hex-devices: use legacy device nameing by default to ease the transition

* hex-devices: hardcode CDSP domain IDs for current devices for now

* hex-optrace: improve multi-NPU timestamp alignment and overall handling of cycle values

* hex-optrace: more robust handling of the fence events
This commit is contained in:
Max Krasnyansky
2026-08-26 18:46:50 -07:00
committed by GitHub
parent 925e117994
commit 192067b72d
44 changed files with 5314 additions and 3139 deletions
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docs/backend/snapdragon/linux.md
+53 -18
View File
@@ -1,25 +1,37 @@
# Snapdragon-based Linux devices
## Docker Setup
The cross-compilation is performed using the Snapdragon Linux Docker toolchain image (see
[github.com/snapdragon-toolchain](https://github.com/snapdragon-toolchain)):
The easiest way to build llama.cpp for a Snapdragon-based Linux device is using the toolchain Docker image (see [github.com/snapdragon-toolchain](https://github.com/snapdragon-toolchain)).
This image includes OpenCL SDK, Hexagon SDK, CMake, and the ARM64 Linux cross-compilation toolchain.
* **Linux toolchain**: `ghcr.io/snapdragon-toolchain/arm64-linux:v0.7`
Cross-compilation is supported on **Linux X86** hosts. The resulting binaries are deployed to and run on the target **Qualcomm Snapdragon ARM64 Linux** device.
```
~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.1
[d]/> cd /workspace
```
Note: The rest of the **Linux** build process assumes that you're running inside the toolchain container.
The unified build utility (`scripts/snapdragon/build.py`) automatically pulls
and orchestrates this container to perform target compilation. You only need to
ensure that Docker is running on your host machine.
## How to Build
Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
### Using build.py script (Recommended)
The easiest way to build llama.cpp is by using the `scripts/snapdragon/build.py` script. It automatically copies the CMake presets,
launches the correct compilation Docker container, builds the libraries and tools,
installs them, and optionally pushes them to your target device.
Build and deploy for a Linux target (using SSH deployment alias `lnx` or `linux`):
```
$ ./scripts/snapdragon/build.py --target lnx:user@host --push
```
### Manual CMake Build
Alternatively, you can build llama.cpp manually by entering the cross-compilation Docker container and running the CMake commands:
```bash
# Start the cross-compilation container manually:
~/src/llama.cpp$ docker run -it --rm -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.7
# Inside the container, build the project using presets:
[d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json .
[d]/workspace> cmake --preset arm64-linux-snapdragon-release -B build-snapdragon
@@ -30,17 +42,19 @@ Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
To generate an installable "package" simply use cmake --install, then zip it:
```
[d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon
[d]/workspace> zip -r pkg-snapdragon.zip pkg-snapdragon
[d]/workspace> cmake --install build-snapdragon --prefix pkg-linux
[d]/workspace> zip -r pkg-linux.zip pkg-linux
```
## How to Install
For this step, you will deploy the built binaries and libraries to the target Linux device. Transfer `pkg-snapdragon.zip` to the target device, then unzip it and set up the environment variables:
For this step, you will deploy the built binaries and libraries to the target
Linux device. Transfer `pkg-linux.zip` to the target device, then unzip it
and set up the environment variables:
```
$ unzip pkg-snapdragon.zip
$ cd pkg-snapdragon
$ unzip pkg-linux.zip
$ cd pkg-linux
$ export LD_LIBRARY_PATH=./lib
$ export ADSP_LIBRARY_PATH=./lib
```
@@ -52,7 +66,28 @@ $ wget https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/
```
## How to Run
Next, since we have setup the environment variables, we can run the llama-cli with the Hexagon backends:
You can run locally on the Snapdragon Linux device:
```
$ ./scripts/snapdragon/run.py --devices HTP0 -- llama-cli -m Llama-3.2-3B-Instruct-Q4_0.gguf -ngl 99 -p "what is the most popular cookie in the world?"
```
Or run remotely from your host development machine using the SSH target option:
```
$ ./scripts/snapdragon/run.py --target lnx:user@host --devices HTP0 -- llama-cli -m Llama-3.2-3B-Instruct-Q4_0.gguf -ngl 99 -p "what is the most popular cookie in the world?"
```
For multi-NPU systems, you can run a tensor split completion command targeting a remote Linux system:
```
$ ./scripts/snapdragon/run.py --target ubuntu:maxk@192.168.1.87 --device HTP0:0,HTP1:0 -- llama-completion -m models/gemma-2b-it-Q4_0.gguf -f prompts/sample_prompt_1024.txt --jinja -st --split-mode tensor --ctx-size 8192
```
This translates to the following command being executed remotely via SSH:
```
+ ssh maxk@192.168.1.87 "cd ~/llama.cpp && ulimit -c unlimited && LD_LIBRARY_PATH=./lib ADSP_LIBRARY_PATH=./lib GGML_HEXAGON_DEVICES=HTP0:0,HTP1:0 GGML_HEXAGON_OPPOLL=1 ./bin/llama-completion -m models/gemma-2b-it-Q4_0.gguf -f prompts/sample_prompt_1024.txt --jinja -st --split-mode tensor --ctx-size 8192 -v -n 16 --device HTP0:0,HTP1:0 -ngl 99 --ubatch-size 1024 -fa on -t 6"
```
Alternatively, you can run the binary directly on the device:
```
$ ./bin/llama-cli -m Llama-3.2-3B-Instruct-Q4_0.gguf --device HTP0 -ngl 99 -p "what is the most popular cookie in the world?"
```