| # Hexagon backend developer details |
|
|
| ## Backend libraries |
|
|
| The Hexagon backend consist of two parts: |
|
|
| - `libggml-hexagon` |
| This is the regular CPU-side GGML backend library, either shared or statically linked |
| |
| - `libggml-htp-vNN` |
| This is the NPU-side (HTP stands for Hexagon Tensor Processor) shared library that contains the Op dispatcher and kernels. |
| The correct library is selected automatically at runtime based on the HW version. |
| |
| Here is an example of the build artifacts |
|
|
| ``` |
| ~/src/llama.cpp$ ls -l pkg-adb/llama.cpp/lib/libggml* |
| pkg-adb/llama.cpp/lib/libggml-base.so |
| pkg-adb/llama.cpp/lib/libggml-cpu.so |
| pkg-adb/llama.cpp/lib/libggml-hexagon.so <<< CPU library |
| pkg-adb/llama.cpp/lib/libggml-htp-v73.so <<< HTP op/kernels for Hexagon v73 |
| pkg-adb/llama.cpp/lib/libggml-htp-v75.so |
| pkg-adb/llama.cpp/lib/libggml-htp-v79.so |
| pkg-adb/llama.cpp/lib/libggml-htp-v81.so |
| ``` |
|
|
| ## Memory buffers |
|
|
| Hexagon NPU backend takes advantage of the Snapdragon's unified memory model where all buffers are fully accessible by the CPU and GPU. |
| The NPU does have a dedicated tightly-coupled memory called VTCM but that memory is used only for intermediate data (e.g. dynamically |
| quantized tensors) or temporary data (chunks of the weight tensors fetched via DMA). |
|
|
| Please note that currently the Hexagon backend does not implement SET/GET_ROWS Ops because there is no advantage in offloading those |
| to the NPU at this point. |
| |
| The backend does allocates non-host buffers for the tensors with datatypes that require repacking: Q4_0, Q8_0, MXFP4. |
| From the MMU perspective these buffers are still regular buffers (normal access by the CPU) they are marked as non-host simply to force |
| the repacking. |
| |
| ## Large model handling |
| |
| Hexagon NPU session (aka Process Domain (PD) in the Hexagon docs) is limited to a memory mapping of around 3.5GB. |
| In llama.cpp/GGML the Hexagon session is mapped to a single GGML backend device (HTP0, HTP1, etc). |
| |
| In order to map models larger than 3.5GB we need to allocate multiple devices and split the model. |
| For this we're taking advantage of the llama.cpp/GGML multi-GPU layer-splitting support. |
| Each Hexagon device behaves like a GPU from the offload and model splitting perspective. |
| |
| Here is an example of running GPT-OSS-20B model on a newer Snapdragon device with 16GB of DDR. |
| |
| ``` |
| M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-completion.sh -f surfing.txt -n 32 |
| ... |
| LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib |
| ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib |
| GGML_HEXAGON_NDEV=4 ./bin/llama-cli --no-mmap -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf |
| -t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 --device HTP0,HTP1,HTP2,HTP3 -no-cnv -f surfing.txt |
| ... |
| llama_model_loader: - type f32: 289 tensors |
| llama_model_loader: - type q4_0: 96 tensors |
| llama_model_loader: - type q8_0: 2 tensors |
| llama_model_loader: - type mxfp4: 72 tensors |
| ... |
| load_tensors: offloaded 25/25 layers to GPU |
| load_tensors: CPU model buffer size = 1182.09 MiB |
| load_tensors: HTP1 model buffer size = 6.64 MiB |
| load_tensors: HTP1-REPACK model buffer size = 2505.94 MiB |
| load_tensors: HTP3 model buffer size = 5.55 MiB |
| load_tensors: HTP3-REPACK model buffer size = 2088.28 MiB |
| load_tensors: HTP0 model buffer size = 7.75 MiB |
| load_tensors: HTP0-REPACK model buffer size = 2923.59 MiB |
| load_tensors: HTP2 model buffer size = 6.64 MiB |
| load_tensors: HTP2-REPACK model buffer size = 2505.94 MiB |
| ... |
| llama_context: n_ctx_per_seq (8192) < n_ctx_train (131072) -- the full capacity of the model will not be utilized |
| llama_context: CPU output buffer size = 0.77 MiB |
| llama_kv_cache_iswa: creating non-SWA KV cache, size = 8192 cells |
| llama_kv_cache: HTP1 KV buffer size = 25.50 MiB |
| llama_kv_cache: HTP3 KV buffer size = 25.50 MiB |
| llama_kv_cache: HTP0 KV buffer size = 25.50 MiB |
| llama_kv_cache: HTP2 KV buffer size = 25.50 MiB |
| llama_kv_cache: size = 102.00 MiB ( 8192 cells, 12 layers, 1/1 seqs), K (q8_0): 51.00 MiB, V (q8_0): 51.00 MiB |
| llama_kv_cache_iswa: creating SWA KV cache, size = 256 cells |
| llama_kv_cache: HTP1 KV buffer size = 0.80 MiB |
| llama_kv_cache: HTP3 KV buffer size = 0.53 MiB |
| llama_kv_cache: HTP0 KV buffer size = 1.06 MiB |
| llama_kv_cache: HTP2 KV buffer size = 0.80 MiB |
| llama_kv_cache: size = 3.19 MiB ( 256 cells, 12 layers, 1/1 seqs), K (q8_0): 1.59 MiB, V (q8_0): 1.59 MiB |
| llama_context: HTP0 compute buffer size = 16.06 MiB |
| llama_context: HTP1 compute buffer size = 16.06 MiB |
| llama_context: HTP2 compute buffer size = 16.06 MiB |
| llama_context: HTP3 compute buffer size = 16.06 MiB |
| llama_context: CPU compute buffer size = 98.19 MiB |
| ... |
| llama_perf_context_print: prompt eval time = 3843.67 ms / 197 tokens ( 19.51 ms per token, 51.25 tokens per second) |
| llama_perf_context_print: eval time = 1686.13 ms / 31 runs ( 54.39 ms per token, 18.39 tokens per second) |
| llama_perf_context_print: total time = 6266.30 ms / 228 tokens |
| llama_perf_context_print: graphs reused = 30 |
| llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | |
| llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 | |
| llama_memory_breakdown_print: | - HTP1 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 | |
| llama_memory_breakdown_print: | - HTP2 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 | |
| llama_memory_breakdown_print: | - HTP3 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 | |
| llama_memory_breakdown_print: | - Host | 1476 = 1208 + 105 + 162 | |
| llama_memory_breakdown_print: | - HTP1-REPACK | 2505 = 2505 + 0 + 0 | |
| llama_memory_breakdown_print: | - HTP3-REPACK | 2088 = 2088 + 0 + 0 | |
| llama_memory_breakdown_print: | - HTP0-REPACK | 2923 = 2923 + 0 + 0 | |
| llama_memory_breakdown_print: | - HTP2-REPACK | 2505 = 2505 + 0 + 0 | |
| ``` |
| |
