mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
Add experimental ggml-hexagon backend for the Hexagon NPU (#16547)
* model: add support for extra bufs for all devices * hexagon: add experimental ggml-hexagon backend for the Hexagon NPU This commit introduces a new experimental backend `ggml-hexagon` with support for the Hexagon NPU. Highlights: - Supports Hexagon versions: v73, v75, v79, and v81 - Targets Android devices based on Snapdragon SoCs: Gen3, 8-Elite, and 8-Elite Gen5 - Supports Q4_0, Q8_0, MXFP4, and FP32 data types - Implements core LLM ops: MUL_MAT/MUL_MAT_ID, ADD/SUB/MUL/ADD_ID, RMS_NORM, ROPE, GLU/SWIGLU, SOFTMAX **Note:** This backend is experimental and may exhibit instability or limited performance across supported devices. It is intended for early testing and feedback from llama.cpp/ggml developer and user community. Co-Authored-By: Rajdeep Ganguly <rganguly@qti.qualcomm.com> Co-Authored-By: Todor Boinovski <todorb@qti.qualcomm.com> * hexagon: fix format checker errors * hexagon: update readme and cmake presets * ci: add android-ndk-build jobs that build plain ARM64 and Snapdragon versions * hexagon: add simple graph optimizer for stacking MUL_MAT ops with the same input * hexagon: move ADB helper scripts into scripts/snapdragon/adb * hexagon: replace all f/printfs with GGML_LOG_... * readme: add hexagon to the list supported backends * hexagon: stack malmuts with quantized inputs only * hexagon: add TODO for fixing issues in hexagon_graph_optimize * hexagon: update to hex-sdk 6.4.0 and add scripts for running on QDC * scripts: fix lint errors * scripts: update qdc pytest script to make linter happy * hexagon: add reduce sum in fp32 * hexagon: reduce number of vector stores in matmul output * hexagon: remove the need for vdelta in reduce-multiply-x8 * hexagon: consistent use of reduce_sum_fp32 for row_sums * hexagon: some more matmul optimizations and comments Optimize cases where tensor dims are not multiple of 1024 (e.g in Qwen models). We've handled those cases already but at a higher overhead. * hexagon: update cmake presets * hexagon: add OPMASK support for run-bench.sh wrapper * hexagon: update to use GGML_BACKEND_API * hexagon: remove unused logic for setting tensor flags for the views * hexagon: add asserts to set/get_tensor to make sure we handle complete tensors Same asserts as the CPU backend. * hexagon: use cpy_tensor slow path for non-host buffers * hexagon: error checks in the buffer allocator * cmake: move include(extProj) under ggml-hexagon * hexagon: don't forget to delete the backend on free * hexagon: set/get_tensor size assert apply only to quantized tensors * hexagon: reintroduce HEX_VERBOSE wrapper for GGML_LOG_DEBUG for now GGML_LOG_DEBUG is always enabled for test-backend-ops and the output gets in the way. Ideally we need a bit more finer log levels. * docs: typos in hexagon developer docs (libggm-...) * hexagon: overhaul error handling in the session/device allocation this should handle all failure paths in the session allocation. * hexagon: update cmake presets to enable fp16 vectors * hexagon: remove unused time_usec function * hexagon: don't forget to release buffer contexts * hexagon: fixed indents in hvx-utils (missed clang-format auto-format failure) * hexagon: remove custom can_repeat function and use ggml_can_repeat --------- Co-authored-by: Rajdeep Ganguly <rganguly@qti.qualcomm.com> Co-authored-by: Todor Boinovski <todorb@qti.qualcomm.com>
This commit is contained in:
Max Krasnyansky
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docs/backend/hexagon/README.md
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docs/backend/hexagon/README.md
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# Snapdragon-based Android devices
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## How to Build
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The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain).
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This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.
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This method works on Linux, macOS, and Windows. macOS and Windows users should install Docker Desktop.
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```
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~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.3
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[d]/> cd /workspace
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```
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The rest of the Android build process assumes that you're running inside the toolchain container.
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Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
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```
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[d]/workspace> cp docs/backend/hexagon/CMakeUserPresets.json .
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[d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
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Preset CMake variables:
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ANDROID_ABI="arm64-v8a"
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...
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CMAKE_TOOLCHAIN_FILE="/opt/android-ndk-r28b/build/cmake/android.toolchain.cmake"
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GGML_HEXAGON="ON"
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GGML_OPENCL="ON"
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GGML_OPENMP="OFF"
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HEXAGON_SDK_ROOT="/opt/hexagon/6.4.0.2"
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...
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-- Including OpenCL backend
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-- Including Hexagon backend
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...
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-- Build files have been written to: /workspace/build-snapdragon
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[d]/workspace> cmake --build build-snapdragon
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...
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[144/356] Performing build step for 'htp-v73'
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[1/16] Generating htp_iface_skel.c, htp_iface_stub.c, htp_iface.h
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[2/16] Building C object CMakeFiles/ggml-htp-v73.dir/hvx-sigmoid.c.obj
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[3/16] Building C object CMakeFiles/ggml-htp-v73.dir/htp-dma.c.obj
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[4/16] Building C object CMakeFiles/ggml-htp-v73.dir/worker-pool.c.obj
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...
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-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v73.so
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-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v75.so
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...
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```
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To generate an installable "package" simply use cmake --install:
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```
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[d]/workspace> cmake --install build-snapdragon --prefix pkg-adb/llama.cpp
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-- Install configuration: "Release"
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-cpu.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-opencl.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-hexagon.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v73.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v75.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v79.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v81.so
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-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml.so
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...
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-- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-bench
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-- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-cli
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...
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```
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## How to Install
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For this step, your device needs to be configured for on-device development.
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Please see https://developer.android.com/studio/debug/dev-options for details.
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Once ADB is enabled, use `adb push` to install `pkg-snapdragon` on the device.
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**Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.**
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```
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~/src/llama.cpp$ adb push pkg-adb/llama.cpp /data/local/tmp/
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pkg-adb/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
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pkg-adb/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
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pkg-adb/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
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102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s)
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```
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At this point, you should also install some models:
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```
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~/src/llama.cpp$ wget https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf
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...
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2025-10-11 12:04:52 (10.7 MB/s) - ‘Llama-3.2-1B-Instruct-Q4_0.gguf’ saved [773025920/773025920]
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~/src/llama.cpp$ adb push Llama-3.2-1B-Instruct-Q4_0.gguf /data/local/tmp/gguf
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Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s)
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```
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## How to Run
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The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables.
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llama.cpp supports three backends on Snapdragon-based devices: CPU, Adreno GPU (GPUOpenCL), and Hexagon NPU (HTP0-4).
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You can select which backend to run the model on using the `D=` variable, which maps to the `--device` option.
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Hexagon NPU behaves as a "GPU" device when it comes to `-ngl` and other offload-related options.
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Here are some examples of running various llama.cpp tools via ADB.
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Simple question for Llama-3.2-1B
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```
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~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-cli.sh -no-cnv -p "what is the most popular cookie in the world?"
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...
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ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
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ggml-hex: Hexagon Arch version v79
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ggml-hex: allocating new session: HTP0
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ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb4000072c7955e50
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...
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load_tensors: offloading output layer to GPU
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load_tensors: offloaded 17/17 layers to GPU
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load_tensors: CPU model buffer size = 225.49 MiB
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load_tensors: HTP0 model buffer size = 0.26 MiB
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load_tensors: HTP0-REPACK model buffer size = 504.00 MiB
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...
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I hope this helps you understand the world's most popular cookies! [end of text]
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...
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llama_perf_sampler_print: sampling time = 30.08 ms / 487 runs ( 0.06 ms per token, 16191.77 tokens per second)
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llama_perf_context_print: load time = 617.94 ms
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llama_perf_context_print: prompt eval time = 80.76 ms / 11 tokens ( 7.34 ms per token, 136.21 tokens per second)
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llama_perf_context_print: eval time = 9210.59 ms / 475 runs ( 19.39 ms per token, 51.57 tokens per second)
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llama_perf_context_print: total time = 9454.92 ms / 486 tokens
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llama_perf_context_print: graphs reused = 473
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llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
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llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
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llama_memory_breakdown_print: | - Host | 439 = 225 + 136 + 77 |
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llama_memory_breakdown_print: | - HTP0-REPACK | 504 = 504 + 0 + 0 |
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```
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Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices
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```
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~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-cli.sh -f surfing.txt -no-cnv
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...
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ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
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ggml-hex: Hexagon Arch version v81
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ggml-hex: allocating new session: HTP0
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ggml-hex: allocating new session: HTP1
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...
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load_tensors: offloading output layer to GPU
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load_tensors: offloaded 17/17 layers to GPU
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load_tensors: CPU model buffer size = 143.86 MiB
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load_tensors: HTP1 model buffer size = 0.23 MiB
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load_tensors: HTP1-REPACK model buffer size = 1575.00 MiB
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load_tensors: HTP0 model buffer size = 0.28 MiB
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load_tensors: HTP0-REPACK model buffer size = 2025.00 MiB
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...
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llama_context: CPU output buffer size = 0.19 MiB
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llama_kv_cache: HTP1 KV buffer size = 238.00 MiB
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llama_kv_cache: HTP0 KV buffer size = 306.00 MiB
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llama_kv_cache: size = 544.00 MiB ( 8192 cells, 16 layers, 1/1 seqs), K (q8_0): 272.00 MiB, V (q8_0): 272.00 MiB
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llama_context: HTP0 compute buffer size = 15.00 MiB
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llama_context: HTP1 compute buffer size = 15.00 MiB
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llama_context: CPU compute buffer size = 24.56 MiB
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...
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llama_perf_context_print: prompt eval time = 1730.57 ms / 212 tokens ( 8.16 ms per token, 122.50 tokens per second)
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llama_perf_context_print: eval time = 5624.75 ms / 257 runs ( 21.89 ms per token, 45.69 tokens per second)
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llama_perf_context_print: total time = 7377.33 ms / 469 tokens
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llama_perf_context_print: graphs reused = 255
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llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
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llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
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llama_memory_breakdown_print: | - HTP1 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
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llama_memory_breakdown_print: | - Host | 742 = 144 + 544 + 54 |
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llama_memory_breakdown_print: | - HTP1-REPACK | 1575 = 1575 + 0 + 0 |
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llama_memory_breakdown_print: | - HTP0-REPACK | 2025 = 2025 + 0 + 0 |
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```
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Op test for MUL_MAT
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```
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~/src/llama.cpp$ HB=0 ./scripts/snapdragon/adb/run-tool.sh test-backend-ops -b HTP0 -o MUL_MAT
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...
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Backend 2/3: HTP0
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Device description: Hexagon
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Device memory: 2048 MB (2048 MB free)
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MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
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MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
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MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
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~/src/llama.cpp-hexagon$ M=Llama-3.2-1B-Instruct-Q4_0.gguf ./scripts/snapdragon/adb/run-bench.sh -p 128 -n 64
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...
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ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
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ggml-hex: Hexagon Arch version v79
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ggml-hex: allocating new session: HTP0
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ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb400007d4b231090
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| model | size | params | backend | ngl | threads | n_batch | mmap | test | t/s |
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| ---------------| ---------: | -----: | ---------- | --: | ------: | ------: | ---: | ----: | ------------: |
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| llama 1B Q4_0 | 729.75 MiB | 1.24 B | HTP | 99 | 4 | 128 | 0 | pp128 | 169.42 ± 1.75 |
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| llama 1B Q4_0 | 729.75 MiB | 1.24 B | HTP | 99 | 4 | 128 | 0 | tg64 | 51.54 ± 1.13 |
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build: 6a8cf8914 (6733)
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```
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## Environment variables
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- `GGML_HEXAGON_NDEV=1`
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Controls the number of devices/sessions to allocate. The default is 1.
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Most quantized models under 4B fit into a single session; an 8B model needs two, and a 20B model needs four.
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- `GGML_HEXAGON_NHVX=0`
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Controls the number of HVX hardware threads to use. The default is all (actual number varies depending on the hardware version).
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- `GGML_HEXAGON_HOSTBUF=1`
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Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
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This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
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- `GGML_HEXAGON_VERBOSE=1`
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Enables verbose logging of Ops from the backend. Example output:
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```
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ggml-hex: HTP0 graph-compute n_nodes 2
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ggml-hex: HTP0 matmul : blk.27.ffn_up.weight x ffn_norm-27 -> ffn_up-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x1
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ggml-hex: HTP0 matmul : blk.27.ffn_gate.weight x ffn_norm-27 -> ffn_gate-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x3
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ggml-hex: HTP0 graph-compute n_nodes 1
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ggml-hex: HTP0 matmul : blk.27.ffn_down.weight x ffn_gate_par-27 -> ffn_out-27 : 8192:3072 x 8192:1 -> 3072:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x0
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ggml-hex: HTP0 get-tensor result_output : data 0x7592487000 offset 0 size 513024
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```
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- `GGML_HEXAGON_PROFILE=1`
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Generates a host-side profile for the ggml-hexagon Ops.
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- `GGML_HEXAGON_OPMASK=0x0`
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Allows enabling specific stages of the processing pipeline:
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- `0x1` Enable Op Queue (i.e., queuing Ops into NPU)
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- `0x2` Enable Dynamic Quantizer (if needed for the Op)
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- `0x4` Enable Op Compute (MUL_MAT, etc.)
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Examples:
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`GGML_HEXAGON_OPMASK=0x1 llama-cli ...` - Ops are enqueued but NPU-side processing is stubbed out
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`GGML_HEXAGON_OPMASK=0x3 llama-cli ...` - NPU performs dynamic quantization and skips the rest
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`GGML_HEXAGON_OPMASK=0x7 llama-cli ...` - Full queuing and processing of Ops (default)
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Reference in New Issue
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