本地推理 llama3 使用 llama.cpp 和 CPU
我的 4090 已经卖掉了，所以在旧电脑上只有这个：
$ sudo nvidia-smi
Sun Sep 1 06:13:48 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.171.04 Driver Version: 535.171.04 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 ... Off | 00000000:01:00.0 Off | N/A |
| N/A 49C P8 10W / 40W | 6MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
只有 6GB 的显存……
不过，我有 32GB 的内存，让我们尝试用 CPU 来推理 llama3！
1. 下载模型
这是 Meta-Llama-3.1-8B 进行 3 位量化的模型。
2. 安装 llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
这将构建 llama.cpp 的 CPU 版本。更多详情请见 https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md。
3. 运行推理
$ ./llama.cpp/llama-simple -m Downloads/Meta-Llama-3.1-8B-Instruct-Q3_K_L.gguf -p "Can you write me a poem about santa cruz?" -n 300
llama_model_loader: loaded meta data with 33 key-value pairs and 292 tensors from Downloads/Meta-Llama-3.1-8B-Instruct-Q3_K_L.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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 32
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 4096
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 13
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - kv 29: quantize.imatrix.file str = /models_out/Meta-Llama-3.1-8B-Instruc...
llama_model_loader: - kv 30: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 31: quantize.imatrix.entries_count i32 = 224
llama_model_loader: - kv 32: quantize.imatrix.chunks_count i32 = 125
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q3_K: 129 tensors
llama_model_loader: - type q5_K: 96 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
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 = 14336
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 = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
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 = 8B
llm_load_print_meta: model ftype = Q3_K - Large
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.02 GiB (4.30 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 8B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: CPU buffer size = 4114.27 MiB
.......................................................................................
llama_new_context_with_model: n_ctx = 131072
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 16384.00 MiB
llama_new_context_with_model: KV self size = 16384.00 MiB, K (f16): 8192.00 MiB, V (f16): 8192.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 MiB
llama_new_context_with_model: CPU compute buffer size = 8480.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
main: n_predict = 300, n_ctx = 131072, n_kv_req = 300
<|begin_of_text|>你能为我写一首关于圣克鲁兹的诗吗？
这是一首关于圣克鲁兹的诗：
圣克鲁兹，海边的小镇
红杉林立，海洋欢腾
浪花拍打在岸边
惊奇在等待，魔法在眼前
滨海步道招手，色彩斑斓
游戏和美食，欢乐无限
咸味太妃糖的香气弥漫
笑声和兴奋无处不在
高耸的山脉，绿色苍翠
徒步者徜徉，自然的秘密可见
河流蜿蜒，水流涓涓
鱼和野生动物茁壮生长，野性中充满光彩
圣克鲁兹，魅力与游戏并存之地
冒险的精神常驻于此
充满生命的城镇，拥有真实的心
梦想在这里成真，魔法在此辉映。
希望你喜欢！如果你有其他请求，请告诉我。
这是修订版的诗，进行了一些修改使其更为简洁流畅：
圣克鲁兹，海边之城
红杉挺立，海洋欢欣
浪迎岸，奇迹在盼
惊奇在等待，魔法在眼前
滨海步道灯光绚烂
游戏和美食，欢乐无限
咸味太妃糖香气弥漫
main: 在 34.22 秒内解码了 289 个标记，速度为 8.44 t/s
llama_print_timings: 加载时间 = 5114.71 ms
llama_print_timings: 采样时间 = 48.04 ms / 290 次运行 ( 0.17 ms 每个标记, 6036.76 个标记每秒)
llama_print_timings: 提示评估时间 = 536.32 ms / 11 个标记 ( 48.76 ms 每个标记, 20.51 个标记每秒)
llama_print_timings: 评估时间 = 33864.35 ms / 289 次运行 ( 117.18 ms 每个标记, 8.53 个标记每秒)
llama_print_timings: 总时间 = 39337.08 ms / 300 个标记
看起来不错！CPU 推理速度还不算太慢，诗写得也很好！
了解更多请访问 https://yunwei37.github.io/My-AI-experiment/ 或者 Github： https://github.com/yunwei37/My-AI-experiment