Llama-cpp를 이용하면 GPU없이도 CPU만으로 일반 컴퓨터에서 Llama2를 실행할 수 있습니다. 이번 글에서는 그 과정을 살펴보겠습니다.
빌드하기
Ubuntu 22.04에서 테스트했습니다.
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
모델 다운로드
아래 사이트에 가면 LlaMa2 모델을 다운로드 받는 방법이 나옵니다.
https://github.com/facebookresearch/llama
Meta 웹사이트에서 등록을해서 메일로 download URL을 받습니다. 받은 URL은 24시간만 유효하니 미리 모두 받는 것을 권장합니다.
https://ai.meta.com/resources/models-and-libraries/llama-downloads/
git clone https://github.com/facebookresearch/codellama
./download.sh "email로 받은 URL"
...
Enter the list of models to download without spaces (7b,13b,34b,7b-Python,13b-Python,34b-Python,7b-Instruct,13b-Instruct,34b-Instruct), or press Enter for all:
모델 변환하기
로컬 PC에서 실행하려면 모델을 변환해야 합니다.
CodeLlama-7b 모델을 예로 하겠습니다.
우선 다운로드 받은 모델을 ggml 형식으로 변환합니다.
python3 convert.py models/CodeLlama-7b/
바로 Inference 해보았습니다. multiply함수를 자바로 생성해주었습니다.
llama.cpp$ ./main -m ./models/CodeLlama-7b/ggml-model-f16.gguf -p "int multiply("
Log start
main: build = 1939 (57744932)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1706076174
llama_model_loader: loaded meta data with 16 key-value pairs and 291 tensors from ./models/CodeLlama-7b/ggml-model-f16.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
..
..
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temp
generate: n_ctx = 512, n_batch = 512, n_predict = -1, n_keep = 0
int multiply(int n1, int n2) {
if (n1 == 0 || n2 == 0) { // base case
return 0;
} else {
return add(multiply(n1, n2 - 1), n1);
}
}
public static int multiply2(int n1, int n2) {
if (n1 == 0 || n2 == 0) { // base case
return 0;
} else {
int result = n1 + multiply2(n1, n2 - 1); // 折半计算
return result;
}
}
public static void main(String[] args) {
System.out.println("0 * 1 = " + multiply(0, 1));
System.out.println("1 * 2 = " + multiply(1, 2));
System.out.println("1 * 3 = " + multiply(1, 3));
System.out.println("1 * 4 = " + multiply(1, 4));
System.out.println("2 * 3 = " + multiply(2, 3));
System.out.println("2 * 5 = " + multiply(2, 5));
System.out.println("3 * 10 = " + multiply(3, 10));
System.out.println("3 * 4 = " + multiply(3, 4));
System.out.println("4 * 8 = " + multiply(4, 8));
System.out.println("5 * 6 = " + multiply(5, 6));
System.out. [end of text]
llama_print_timings: load time = 59120.92 ms
llama_print_timings: sample time = 49.16 ms / 398 runs ( 0.12 ms per token, 8095.19 tokens per second)
llama_print_timings: prompt eval time = 427.50 ms / 4 tokens ( 106.88 ms per token, 9.36 tokens per second)
llama_print_timings: eval time = 116470.88 ms / 397 runs ( 293.38 ms per token, 3.41 tokens per second)
llama_print_timings: total time = 117027.28 ms / 401 tokens
Log end
그런데 1분정도 시간이 걸렸습니다. 이제 4비트 모델로 변환해서 성능을 높여보겠습니다.
4비트 모델로 변환하기 (Quantization)
./quantize ./models/CodeLlama-7b/ggml-model-f16.gguf ./models/CodeLlama-7b/ggml-model-q4_0.gguf q4_0
Inference 해보기
-n 으로 token수를 지정할 수 있습니다.
llama.cpp$ ./main -m ./models/CodeLlama-7b/ggml-model-q4_0.gguf -n 256 -p "int multiply("
Log start
main: build = 1939 (57744932)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1706076680
llama_model_loader: loaded meta data with 17 key-value pairs and 291 tensors from ./models/CodeLlama-7b/ggml-model-q4_0.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.name str = models
llama_model_loader: - kv 2: llama.context_length u32 = 16384
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 11: general.file_type u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32016] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32016] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32016] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 264/32016 vs 259/32016 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32016
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
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: n_ff = 11008
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
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_yarn_orig_ctx = 16384
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 3.56 GiB (4.54 BPW)
llm_load_print_meta: general.name = models
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temp
generate: n_ctx = 512, n_batch = 512, n_predict = 256, n_keep = 0
int multiply(int a, int b) {
return a * b;
}
int divide(int a, int b) {
return a / b;
}
```
We can add some simple validation to the function to make sure the input values are sane. We'll just ensure that `a` and `b` are greater than zero:
```csharp
[DllImport("MyMath")]
public static extern int multiply(int a, int b);
[DllImport("MyMath")]
public static extern int divide(int a, int b);
// Note that we've added the [SuppressUnmanagedCodeSecurity] attribute here.
[SuppressUnmanagedCodeSecurity]
unsafe private static TDelegate LoadFunction<TDelegate>(string functionName) where TDelegate : class {
var ptr = GetProcAddress(LoadLibrary("MyMath"), functionName);
if (ptr == IntPtr.Zero)
throw new InvalidOperationException($"Failed to get address for: '{functionName}'");
var fp = Marshal.GetDelegateForFunctionPointer<TDelegate>(ptr);
llama_print_timings: load time = 227.93 ms
llama_print_timings: sample time = 33.48 ms / 256 runs ( 0.13 ms per token, 7647.27 tokens per second)
llama_print_timings: prompt eval time = 144.80 ms / 4 tokens ( 36.20 ms per token, 27.62 tokens per second)
llama_print_timings: eval time = 24956.50 ms / 255 runs ( 97.87 ms per token, 10.22 tokens per second)
llama_print_timings: total time = 25187.38 ms / 259 tokens
Log end
이번에는 C# 코드가 나왔네요. finetuning이 안된 모델이라서 아무렇게나(?) 코드가 생성됩니다. 4bit로 quantization를 하니까 성능이 2배 정도 빨라졌습니다.
13478367200 Jan 23 19:47 ggml-model-f16.gguf
3825898016 Jan 23 19:52 ggml-model-q4_0.gguf
모델 크기는 3배 이상 줄어들었습니다. 32비트를 4비트로 줄였으니, 당연한 결과겠지요.
참고
* https://blog.gopenai.com/how-to-run-llama-2-and-code-llama-on-your-laptop-without-gpu-3ab68dd15d4a
