源码编译llama.cpp for android
我这有已经编译好的版本，直接下载使用：
https://github.com/turingevo/llama.cpp-build/releases/tag/b4331
准备 android-ndk
已下载：
bash
/media/wmx/ws1/software/qtAndroid/Sdk/ndk/23.1.7779620版本 ： llama.cpp-b4331
下载源码
切换到 llama.cpp/目录
编译脚本 llama.cpp/build-android.sh
bash
#!/bin/bash
ANDROID_NDK_PATH=/media/wmx/ws1/software/qtAndroid/Sdk/ndk/23.1.7779620
build_dir=build-android
src_dir=.
install_dir=bin/android
cmake \
-DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_PATH}/build/cmake/android.toolchain.cmake \
-DANDROID_ABI=arm64-v8a \
-DANDROID_PLATFORM=android-28 \
-DCMAKE_C_FLAGS="-march=armv8.7a" \
-DCMAKE_CXX_FLAGS="-march=armv8.7a" \
-DGGML_OPENMP=OFF \
-DGGML_LLAMAFILE=OFF \
-B ${build_dir} \
-S ${src_dir}
cmake --build ${build_dir} --config Release -j48
cmake --install ${build_dir} --prefix ${install_dir} --config Releasepush 到android设备测试
下面是 华为mate40pro 上的测试结果
build llama.cpp/bin/android
shell
adb shell "mkdir /data/local/tmp/llama.cpp"
adb push bin/android /data/local/tmp/llama.cpp/
adb push qwen2.5-0.5b-instruct-q4_k_m.gguf /data/local/tmp/llama.cpp/
adb shell
cd /data/local/tmp/llama.cpp/android
touch test.sh
chmod a+x test.sh
cat " LD_LIBRARY_PATH=lib ./bin/llama-simple -m qwen2.5-0.5b-instruct-q4_k_m.gguf -p \"你是谁?\" " > test.sh
./test.sh
HWNOH:/data/local/tmp/llama.cpp/android $ ./test.sh
llama_model_loader: loaded meta data with 26 key-value pairs and 291 tensors from /sdcard/a-wmx/models/qwen2.5-0.5b-instruct-q4_k_m.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = qwen2.5-0.5b-instruct
llama_model_loader: - kv 3: general.version str = v0.1
llama_model_loader: - kv 4: general.finetune str = qwen2.5-0.5b-instruct
llama_model_loader: - kv 5: general.size_label str = 630M
llama_model_loader: - kv 6: qwen2.block_count u32 = 24
llama_model_loader: - kv 7: qwen2.context_length u32 = 32768
llama_model_loader: - kv 8: qwen2.embedding_length u32 = 896
llama_model_loader: - kv 9: qwen2.feed_forward_length u32 = 4864
llama_model_loader: - kv 10: qwen2.attention.head_count u32 = 14
llama_model_loader: - kv 11: qwen2.attention.head_count_kv u32 = 2
llama_model_loader: - kv 12: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 14: general.file_type u32 = 15
llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 16: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 121 tensors
llama_model_loader: - type q5_0: 133 tensors
llama_model_loader: - type q8_0: 13 tensors
llama_model_loader: - type q4_K: 12 tensors
llama_model_loader: - type q6_K: 12 tensors
llm_load_vocab: control token: 151659 '<|fim_prefix|>' is not marked as EOG
llm_load_vocab: control token: 151656 '<|video_pad|>' is not marked as EOG
llm_load_vocab: control token: 151655 '<|image_pad|>' is not marked as EOG
llm_load_vocab: control token: 151653 '<|vision_end|>' is not marked as EOG
llm_load_vocab: control token: 151652 '<|vision_start|>' is not marked as EOG
llm_load_vocab: control token: 151651 '<|quad_end|>' is not marked as EOG
llm_load_vocab: control token: 151649 '<|box_end|>' is not marked as EOG
llm_load_vocab: control token: 151648 '<|box_start|>' is not marked as EOG
llm_load_vocab: control token: 151646 '<|object_ref_start|>' is not marked as EOG
llm_load_vocab: control token: 151644 '<|im_start|>' is not marked as EOG
llm_load_vocab: control token: 151661 '<|fim_suffix|>' is not marked as EOG
llm_load_vocab: control token: 151647 '<|object_ref_end|>' is not marked as EOG
llm_load_vocab: control token: 151660 '<|fim_middle|>' is not marked as EOG
llm_load_vocab: control token: 151654 '<|vision_pad|>' is not marked as EOG
llm_load_vocab: control token: 151650 '<|quad_start|>' is not marked as EOG
llm_load_vocab: special tokens cache size = 22
llm_load_vocab: token to piece cache size = 0.9310 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 896
llm_load_print_meta: n_layer = 24
llm_load_print_meta: n_head = 14
llm_load_print_meta: n_head_kv = 2
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 128
llm_load_print_meta: n_embd_v_gqa = 128
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 4864
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 1B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 630.17 M
llm_load_print_meta: model size = 462.96 MiB (6.16 BPW)
llm_load_print_meta: general.name = qwen2.5-0.5b-instruct
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: FIM PRE token = 151659 '<|fim_prefix|>'
llm_load_print_meta: FIM SUF token = 151661 '<|fim_suffix|>'
llm_load_print_meta: FIM MID token = 151660 '<|fim_middle|>'
llm_load_print_meta: FIM PAD token = 151662 '<|fim_pad|>'
llm_load_print_meta: FIM REP token = 151663 '<|repo_name|>'
llm_load_print_meta: FIM SEP token = 151664 '<|file_sep|>'
llm_load_print_meta: EOG token = 151643 '<|endoftext|>'
llm_load_print_meta: EOG token = 151645 '<|im_end|>'
llm_load_print_meta: EOG token = 151662 '<|fim_pad|>'
llm_load_print_meta: EOG token = 151663 '<|repo_name|>'
llm_load_print_meta: EOG token = 151664 '<|file_sep|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: tensor 'token_embd.weight' (q5_0) (and 290 others) cannot be used with preferred buffer type CPU_AARCH64, using CPU instead
llm_load_tensors: CPU_Mapped model buffer size = 462.96 MiB
.....................................................
llama_new_context_with_model: n_batch is less than GGML_KQ_MASK_PAD - increasing to 32
llama_new_context_with_model: n_seq_max = 1
llama_new_context_with_model: n_ctx = 64
llama_new_context_with_model: n_ctx_per_seq = 64
llama_new_context_with_model: n_batch = 32
llama_new_context_with_model: n_ubatch = 32
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_per_seq (64) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_kv_cache_init: CPU KV buffer size = 0.75 MiB
llama_new_context_with_model: KV self size = 0.75 MiB, K (f16): 0.38 MiB, V (f16): 0.38 MiB
llama_new_context_with_model: CPU output buffer size = 0.58 MiB
llama_new_context_with_model: CPU compute buffer size = 18.66 MiB
llama_new_context_with_model: graph nodes = 846
llama_new_context_with_model: graph splits = 1
-p 你是谁?我是阿里云开发的超大规模语言模型，我叫通义千问。通义是"通义天下"，千问是"千问天下
main: decoded 32 tokens in 2.21 s, speed: 14.49 t/s
llama_perf_sampler_print: sampling time = 5.69 ms / 32 runs ( 0.18 ms per token, 5622.91 tokens per second)
llama_perf_context_print: load time = 1907.15 ms
llama_perf_context_print: prompt eval time = 165.11 ms / 5 tokens ( 33.02 ms per token, 30.28 tokens per second)
llama_perf_context_print: eval time = 2000.08 ms / 31 runs ( 64.52 ms per token, 15.50 tokens per second)
llama_perf_context_print: total time = 3950.19 ms / 36 tokens