1 背景

从零开始学大模型之——LLaMa2-7B。

2 搭建环境

anaconda的按照教程请参考：
Linux安装conda
conda离线安装pytorch

2.1 硬件配置

系统：windows 11
内存：48GB
显卡：RTX 4070,12GB
处理器：i5-13600KF

2.2 搭建虚拟环境

2.2.1 创建虚拟环境

conda create --name llama2 python=3.10

2.2.2 安装所需的库

安装torch：

conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia

安装其他库：

pip install numpy==1.23.5 pytest Requests sentencepiece tqdm wandb

3 准备工作

3.1 下载GitHub代码

git clone https://github.com/karpathy/llama2.c.git

3.2 下载模型

到huggingface网站上下载Llama-2-7b-chat-hf模型。
需要科学上网，然后注册账号，申请权限通过后即可下载。

由于模型数据都来源于huggingface，从huggingface下载模型权重和训练数据过程中可能会遇到各种网络问题，可以下载时通过走huggingface的国内镜像hf-mirror加快下载速度，然后使用huggingface-cli进行模型文件和数据的下载。

具体操作请参考网上其他资料，此处仅提供部分操作代码：

# 下载huggingface_hub 
 pip install -U huggingface_hub
 
 # 设置环境变量
 export HF_ENDPOINT=https://hf-mirror.com
 
 # 下载模型，指定模型的保存位置
 huggingface-cli download --resume-download NousResearch/Meta-Llama-3-8B --local-dir ./model/Meta-Llama-3-8B

3.3 数据处理

3.3.1 下载数据

python tinystories.py download

3.3.2 数据集tokenize预处理

python tinystories.py pretokenize

4 训练

4.1 修改配置

train.py里面有几个参数要修改
batch_size改小一点，否则会报’CUDA out of memory’ 的错误；
dtype要改为"float16"，否则会报’Current CUDA Device does not support bfloat16’的错误；
compile要改为False，否则会报CUDA Capability过低或complex64不支持的错误。

batch_size = 64
dtype = "float16"
compile = False

可选改的参数：
max_iters：是迭代次数，可改小一点。
warmup_iters：是热身的迭代次数，主要是为了确定合适得学习率，卡有限的话可改小一些。

max_iters = 100000
warmup_iters = 1000

4.2 开始训练

python train.py

我设置的参数：

batch_size = 128  # if gradient_accumulation_steps > 1, this is the micro-batch size
max_iters = 100000  # total number of training iterations
warmup_iters = 1000  # how many steps to warm up for
dtype = "float16"  # float32|bfloat16|float16
compile = False  # use PyTorch 2.0 to compile the model to be faster

Loss：

显存占用情况：

训练结束后，会在out文件夹下保存ckpt：

4.3 多机多卡训练

可以在命令行中指定训练参数(单GPU上训练)：

python -m train.py --compile=False --eval_iters=100 --batch_size=64

如果是多GPU，可采用分布式训练，例如采用DDP 在1个node，2个 gpu 上训练：

torchrun --standalone --nproc_per_node=2 train.py

采用DDP 在2个node，8个 gpu 上训练：

torchrun --nproc_per_node=4 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py

5 模型推理

注意：Linux跑没问题，Windows跑没输出，暂时还没解决Windows的问题。

5.1 编译

如果是Linux，直接make即可。

gcc run.c -o run -lm

如果是windows，需要先安装gcc，然后修改run.c代码，最后再生成可执行文件。

5.1.1 安装gcc

在gcc官网下载压缩文件，解压到本地。

配置环境变量：

• 解压完成后，需要将MinGW-w64的bin目录添加到系统的PATH环境变量中。
• 打开“控制面板”，选择“系统和安全”，然后选择“系统”。
• 点击左侧的“高级系统设置”，在弹出的对话框中点击“环境变量”。
• 在“系统变量”区域，找到Path变量，选中后点击“编辑”。
• 点击“新建”，然后将MinGW-w64的bin目录路径添加进去，例如C:\Program Files\mingw-w64\mingw64\bin。
• 确认所有对话框并关闭。

测试：在终端输入

gcc --version

没有报错说明安装成功。

5.1.2 修改run.c代码

在 Windows 系统上，由于 mmap 和 munmap 不是标准的 Win32 API 函数，需要使用相应的 Win32 函数来实现类似的功能。可以使用 CreateFileMapping 和 MapViewOfFile 来代替 mmap，使用 UnmapViewOfFile 来代替 munmap。
将以下代码拷贝覆盖原文件即可。

/* Inference for Llama-2 Transformer model in pure C */

#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <time.h>
#include <math.h>
#include <string.h>
#include <fcntl.h>
#if defined _WIN32
    #include "win.h"
#else
    #include <unistd.h>
    #include <sys/mman.h>
#endif
// ----------------------------------------------------------------------------
// Transformer model

typedef struct {
    int dim; // transformer dimension
    int hidden_dim; // for ffn layers
    int n_layers; // number of layers
    int n_heads; // number of query heads
    int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
    int vocab_size; // vocabulary size, usually 256 (byte-level)
    int seq_len; // max sequence length
} Config;

typedef struct {
    // token embedding table
    float* token_embedding_table;    // (vocab_size, dim)
    // weights for rmsnorms
    float* rms_att_weight; // (layer, dim) rmsnorm weights
    float* rms_ffn_weight; // (layer, dim)
    // weights for matmuls. note dim == n_heads * head_size
    float* wq; // (layer, dim, n_heads * head_size)
    float* wk; // (layer, dim, n_kv_heads * head_size)
    float* wv; // (layer, dim, n_kv_heads * head_size)
    float* wo; // (layer, n_heads * head_size, dim)
    // weights for ffn
    float* w1; // (layer, hidden_dim, dim)
    float* w2; // (layer, dim, hidden_dim)
    float* w3; // (layer, hidden_dim, dim)
    // final rmsnorm
    float* rms_final_weight; // (dim,)
    // (optional) classifier weights for the logits, on the last layer
    float* wcls;
} TransformerWeights;

typedef struct {
    // current wave of activations
    float *x; // activation at current time stamp (dim,)
    float *xb; // same, but inside a residual branch (dim,)
    float *xb2; // an additional buffer just for convenience (dim,)
    float *hb; // buffer for hidden dimension in the ffn (hidden_dim,)
    float *hb2; // buffer for hidden dimension in the ffn (hidden_dim,)
    float *q; // query (dim,)
    float *k; // key (dim,)
    float *v; // value (dim,)
    float *att; // buffer for scores/attention values (n_heads, seq_len)
    float *logits; // output logits
    // kv cache
    float* key_cache;   // (layer, seq_len, dim)
    float* value_cache; // (layer, seq_len, dim)
} RunState;

typedef struct {
    Config config; // the hyperparameters of the architecture (the blueprint)
    TransformerWeights weights; // the weights of the model
    RunState state; // buffers for the "wave" of activations in the forward pass
    // some more state needed to properly clean up the memory mapping (sigh)
    HANDLE fileHandle; // file handle for memory mapping
    void* data; // memory mapped data pointer
    ssize_t file_size; // size of the checkpoint file in bytes
} Transformer;

void malloc_run_state(RunState* s, Config* p) {
    // we calloc instead of malloc to keep valgrind happy
    int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
    s->x = calloc(p->dim, sizeof(float));
    s->xb = calloc(p->dim, sizeof(float));
    s->xb2 = calloc(p->dim, sizeof(float));
    s->hb = calloc(p->hidden_dim, sizeof(float));
    s->hb2 = calloc(p->hidden_dim, sizeof(float));
    s->q = calloc(p->dim, sizeof(float));
    s->key_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
    s->value_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
    s->att = calloc(p->n_heads * p->seq_len, sizeof(float));
    s->logits = calloc(p->vocab_size, sizeof(float));
    // ensure all mallocs went fine
    if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q
     || !s->key_cache || !s->value_cache || !s->att || !s->logits) {
        fprintf(stderr, "malloc failed!\n");
        exit(EXIT_FAILURE);
    }
}

void free_run_state(RunState* s) {
    free(s->x);
    free(s->xb);
    free(s->xb2);
    free(s->hb);
    free(s->hb2);
    free(s->q);
    free(s->att);
    free(s->logits);
    free(s->key_cache);
    free(s->value_cache);
}

void memory_map_weights(TransformerWeights *w, Config* p, float* ptr, int shared_weights) {
    int head_size = p->dim / p->n_heads;
    // make sure the multiplications below are done in 64bit to fit the parameter counts of 13B+ models
    unsigned long long n_layers = p->n_layers;
    w->token_embedding_table = ptr;
    ptr += p->vocab_size * p->dim;
    w->rms_att_weight = ptr;
    ptr += n_layers * p->dim;
    w->wq = ptr;
    ptr += n_layers * p->dim * (p->n_heads * head_size);
    w->wk = ptr;
    ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
    w->wv = ptr;
    ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
    w->wo = ptr;
    ptr += n_layers * (p->n_heads * head_size) * p->dim;
    w->rms_ffn_weight = ptr;
    ptr += n_layers * p->dim;
    w->w1 = ptr;
    ptr += n_layers * p->dim * p->hidden_dim;
    w->w2 = ptr;
    ptr += n_layers * p->hidden_dim * p->dim;
    w->w3 = ptr;
    ptr += n_layers * p->dim * p->hidden_dim;
    w->rms_final_weight = ptr;
    ptr += p->dim;
    ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_real (for RoPE)
    ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_imag (for RoPE)
    w->wcls = shared_weights ? w->token_embedding_table : ptr;
}

void read_checkpoint(char* checkpoint, Config* config, TransformerWeights* weights,
                     HANDLE* fileHandle, void** data, ssize_t* file_size) {
    // read in the config header
    FILE *file = fopen(checkpoint, "rb");
    if (!file) { fprintf(stderr, "Couldn't open file %s\n", checkpoint); exit(EXIT_FAILURE); }
    if (fread(config, sizeof(Config), 1, file) != 1) { exit(EXIT_FAILURE); }
    // negative vocab size is hacky way of signaling unshared weights. bit yikes.
    int shared_weights = config->vocab_size > 0 ? 1 : 0;
    config->vocab_size = abs(config->vocab_size);
    // figure out the file size
    fseek(file, 0, SEEK_END); // move file pointer to end of file
    *file_size = ftell(file); // get the file size, in bytes
    fclose(file);
    // open the file
    *fileHandle = CreateFile(checkpoint, GENERIC_READ, FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL);
    if (*fileHandle == INVALID_HANDLE_VALUE) {
        fprintf(stderr, "open failed!\n");
        exit(EXIT_FAILURE);
    }
    // create a file mapping object
    HANDLE hMapFile = CreateFileMapping(*fileHandle, NULL, PAGE_READONLY, 0, 0, NULL);
    if (hMapFile == NULL) {
        fprintf(stderr, "CreateFileMapping failed!\n");
        CloseHandle(*fileHandle);
        exit(EXIT_FAILURE);
    }
    // map the view of the file
    *data = MapViewOfFile(hMapFile, FILE_MAP_READ, 0, 0, 0);
    if (*data == NULL) {
        fprintf(stderr, "MapViewOfFile failed!\n");
        CloseHandle(hMapFile);
        CloseHandle(*fileHandle);
        exit(EXIT_FAILURE);
    }
    float* weights_ptr = *data + sizeof(Config)/sizeof(float);
    memory_map_weights(weights, config, weights_ptr, shared_weights);
}

void build_transformer(Transformer *t, char* checkpoint_path) {
    // read in the Config and the Weights from the checkpoint
    read_checkpoint(checkpoint_path, &t->config, &t->weights, &t->fileHandle, &t->data, &t->file_size);
    // allocate the RunState buffers
    malloc_run_state(&t->state, &t->config);
}

void free_transformer(Transformer* t) {
    // close the memory mapping
    if (t->data != NULL) { UnmapViewOfFile(t->data); }
    if (t->fileHandle != INVALID_HANDLE_VALUE) { CloseHandle(t->fileHandle); }
    // free the RunState buffers
    free_run_state(&t->state);
}

// ----------------------------------------------------------------------------
// neural net blocks; the dynamics of the Transformer

void rmsnorm(float* o, float* x, float* weight, int size) {
    // calculate sum of squares
    float ss = 0.0f;
    for (int j = 0; j < size; j++) {
        ss += x[j] * x[j];
    }
    ss /= size;
    ss += 1e-5f;
    ss = 1.0f / sqrtf(ss);
    // normalize and scale
    for (int j = 0; j < size; j++) {
        o[j] = weight[j] * (ss * x[j]);
    }
}

void softmax(float* x, int size) {
    // find max value (for numerical stability)
    float max_val = x[0];
    for (int i = 1; i < size; i++) {
        if (x[i] > max_val) {
            max_val = x[i];
        }
    }
    // exp and sum
    float sum = 0.0f;
    for (int i = 0; i < size; i++) {
        x[i] = expf(x[i] - max_val);
        sum += x[i];
    }
    // normalize
    for (int i = 0; i < size; i++) {
        x[i] /= sum;
    }
}

void matmul(float* xout, float* x, float* w, int n, int d) {
    // W (d,n) @ x (n,) -> xout (d,)
    // by far the most amount of time is spent inside this little function
    int i;
    #pragma omp parallel for private(i)
    for (i = 0; i < d; i++) {
        float val = 0.0f;
        for (int j = 0; j < n; j++) {
            val += w[i * n + j] * x[j];
        }
        xout[i] = val;
    }
}

float* forward(Transformer* transformer, int token, int pos) {

    // a few convenience variables
    Config* p = &transformer->config;
    TransformerWeights* w = &transformer->weights;
    RunState* s = &transformer->state;
    float *x = s->x;
    int dim = p->dim;
    int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
    int kv_mul = p->n_heads / p->n_kv_heads; // integer multiplier of the kv sharing in multiquery
    int hidden_dim =  p->hidden_dim;
    int head_size = dim / p->n_heads;

    // copy the token embedding into x
    float* content_row = w->token_embedding_table + token * dim;
    memcpy(x, content_row, dim*sizeof(*x));

    // forward all the layers
    for(unsigned long long l = 0; l < p->n_layers; l++) {

        // attention rmsnorm
        rmsnorm(s->xb, x, w->rms_att_weight + l*dim, dim);

        // key and value point to the kv cache
        int loff = l * p->seq_len * kv_dim; // kv cache layer offset for convenience
        s->k = s->key_cache + loff + pos * kv_dim;
        s->v = s->value_cache + loff + pos * kv_dim;

        // qkv matmuls for this position
        matmul(s->q, s->xb, w->wq + l*dim*dim, dim, dim);
        matmul(s->k, s->xb, w->wk + l*dim*kv_dim, dim, kv_dim);
        matmul(s->v, s->xb, w->wv + l*dim*kv_dim, dim, kv_dim);

        // RoPE relative positional encoding: complex-valued rotate q and k in each head
        for (int i = 0; i < dim; i+=2) {
            int head_dim = i % head_size;
            float freq = 1.0f / powf(10000.0f, head_dim / (float)head_size);
            float val = pos * freq;
            float fcr = cosf(val);
            float fci = sinf(val);
            int rotn = i < kv_dim ? 2 : 1; // how many vectors? 2 = q & k, 1 = q only
            for (int v = 0; v < rotn; v++) {
                float* vec = v == 0 ? s->q : s->k; // the vector to rotate (query or key)
                float v0 = vec[i];
                float v1 = vec[i+1];
                vec[i]   = v0 * fcr - v1 * fci;
                vec[i+1] = v0 * fci + v1 * fcr;
            }
        }

        // multihead attention. iterate over all heads
        int h;
        #pragma omp parallel for private(h)
        for (h = 0; h < p->n_heads; h++) {
            // get the query vector for this head
            float* q = s->q + h * head_size;
            // attention scores for this head
            float* att = s->att + h * p->seq_len;
            // iterate over all timesteps, including the current one
            for (int t = 0; t <= pos; t++) {
                // get the key vector for this head and at this timestep
                float* k = s->key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
                // calculate the attention score as the dot product of q and k
                float score = 0.0f;
                for (int i = 0; i < head_size; i++) {
                    score += q[i] * k[i];
                }
                score /= sqrtf(head_size);
                // save the score to the attention buffer
                att[t] = score;
            }

            // softmax the scores to get attention weights, from 0..pos inclusively
            softmax(att, pos + 1);

            // weighted sum of the values, store back into xb
            float* xb = s->xb + h * head_size;
            memset(xb, 0, head_size * sizeof(float));
            for (int t = 0; t <= pos; t++) {
                // get the value vector for this head and at this timestep
                float* v = s->value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
                // get the attention weight for this timestep
                float a = att[t];
                // accumulate the weighted value into xb
                for (int i = 0; i < head_size; i++) {
                    xb[i] += a * v[i];
                }
            }
        }

        // final matmul to get the output of the attention
        matmul(s->xb2, s->xb, w->wo + l*dim*dim, dim, dim);

        // residual connection back into x
        for (int i = 0; i < dim; i++) {
            x[i] += s->xb2[i];
        }

        // ffn rmsnorm
        rmsnorm(s->xb, x, w->rms_ffn_weight + l*dim, dim);

        // Now for FFN in PyTorch we have: self.w2(F.silu(self.w1(x)) * self.w3(x))
        // first calculate self.w1(x) and self.w3(x)
        matmul(s->hb, s->xb, w->w1 + l*dim*hidden_dim, dim, hidden_dim);
        matmul(s->hb2, s->xb, w->w3 + l*dim*hidden_dim, dim, hidden_dim);

        // SwiGLU non-linearity
        for (int i = 0; i < hidden_dim; i++) {
            float val = s->hb[i];
            // silu(x)=x*σ(x), where σ(x) is the logistic sigmoid
            val *= (1.0f / (1.0f + expf(-val)));
            // elementwise multiply with w3(x)
            val *= s->hb2[i];
            s->hb[i] = val;
        }

        // final matmul to get the output of the ffn
        matmul(s->xb, s->hb, w->w2 + l*dim*hidden_dim, hidden_dim, dim);

        // residual connection
        for (int i = 0; i < dim; i++) {
            x[i] += s->xb[i];
        }
    }

    // final rmsnorm
    rmsnorm(x, x, w->rms_final_weight, dim);

    // classifier into logits
    matmul(s->logits, x, w->wcls, p->dim, p->vocab_size);
    return s->logits;
}

// ----------------------------------------------------------------------------
// The Byte Pair Encoding (BPE) Tokenizer that translates strings <-> tokens

typedef struct {
    char *str;
    int id;
} TokenIndex;

typedef struct {
    char** vocab;
    float* vocab_scores;
    TokenIndex *sorted_vocab;
    int vocab_size;
    unsigned int max_token_length;
    unsigned char byte_pieces[512]; // stores all single-byte strings
} Tokenizer;

int compare_tokens(const void *a, const void *b) {
    return strcmp(((TokenIndex*)a)->str, ((TokenIndex*)b)->str);
}

void build_tokenizer(Tokenizer* t, char* tokenizer_path, int vocab_size) {
    // i should have written the vocab_size into the tokenizer file... sigh
    t->vocab_size = vocab_size;
    // malloc space to hold the scores and the strings
    t->vocab = (char**)malloc(vocab_size * sizeof(char*));
    t->vocab_scores = (float*)malloc(vocab_size * sizeof(float));
    t->sorted_vocab = NULL; // initialized lazily
    for (int i = 0; i < 256; i++) {
        t->byte_pieces[i * 2] = (unsigned char)i;
        t->byte_pieces[i * 2 + 1] = '\0';
    }
    // read in the file
    FILE *file = fopen(tokenizer_path, "rb");
    if (!file) { fprintf(stderr, "couldn't load %s\n", tokenizer_path); exit(EXIT_FAILURE); }
    if (fread(&t->max_token_length, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
    int len;
    for (int i = 0; i < vocab_size; i++) {
        if (fread(t->vocab_scores + i, sizeof(float), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE);}
        if (fread(&len, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
        t->vocab[i] = (char *)malloc(len + 1);
        if (fread(t->vocab[i], len, 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
        t->vocab[i][len] = '\0'; // add the string terminating token
    }
    fclose(file);
}

void free_tokenizer(Tokenizer* t) {
    for (int i = 0; i < t->vocab_size; i++) { free(t->vocab[i]); }
    free(t->vocab);
    free(t->vocab_scores);
    free(t->sorted_vocab);
}

char* decode(Tokenizer* t, int prev_token, int token) {
    char *piece = t->vocab[token];
    // following BOS (1) token, sentencepiece decoder strips any leading whitespace (see PR #89)
    if (prev_token == 1 && piece[0] == ' ') { piece++; }
    // careful, some tokens designate raw bytes, and look like e.g. '<0x01>'
    // parse this and convert and return the actual byte
    unsigned char byte_val;
    if (sscanf(piece, "<0x%02hhX>", &byte_val) == 1) {
        piece = (char*)t->byte_pieces + byte_val * 2;
    }
    return piece;
}

void safe_printf(char *piece) {
    // piece might be a raw byte token, and we only want to print printable chars or whitespace
    // because some of the other bytes can be various control codes, backspace, etc.
    if (piece == NULL) { return; }
    if (piece[0] == '\0') { return; }
    if (piece[1] == '\0') {
        unsigned char byte_val = piece[0];
        if (!(isprint(byte_val) || isspace(byte_val))) {
            return; // bad byte, don't print it
        }
    }
    printf("%s", piece);
}

int str_lookup(char *str, TokenIndex *sorted_vocab, int vocab_size) {
    // efficiently find the perfect match for str in vocab, return its index or -1 if not found
    TokenIndex tok = { .str = str }; // acts as the key to search for
    TokenIndex *res = bsearch(&tok, sorted_vocab, vocab_size, sizeof(TokenIndex), compare_tokens);
    return res != NULL ? res->id : -1;
}

void encode(Tokenizer* t, char *text, int8_t bos, int8_t eos, int *tokens, int *n_tokens) {
    // encode the string text (input) into an upper-bound preallocated tokens[] array
    // bos != 0 means prepend the BOS token (=1), eos != 0 means append the EOS token (=2)
    if (text == NULL) { fprintf(stderr, "cannot encode NULL text\n"); exit(EXIT_FAILURE); }

    if (t->sorted_vocab == NULL) {
        // lazily malloc and sort the vocabulary
        t->sorted_vocab = malloc(t->vocab_size * sizeof(TokenIndex));
        for (int i = 0; i < t->vocab_size; i++) {
            t->sorted_vocab[i].str = t->vocab[i];
            t->sorted_vocab[i].id = i;
        }
        qsort(t->sorted_vocab, t->vocab_size, sizeof(TokenIndex), compare_tokens);
    }

    // create a temporary buffer that will store merge candidates of always two consecutive tokens
    // *2 for concat, +1 for null terminator +2 for UTF8 (in case max_token_length is 1)
    char* str_buffer = malloc((t->max_token_length*2 +1 +2) * sizeof(char));
    size_t str_len = 0;

    // start at 0 tokens
    *n_tokens = 0;

    // add optional BOS (=1) token, if desired
    if (bos) tokens[(*n_tokens)++] = 1;

    // add_dummy_prefix is true by default
    // so prepend a dummy prefix token to the input string, but only if text != ""
    // TODO: pretty sure this isn't correct in the general case but I don't have the
    // energy to read more of the sentencepiece code to figure out what it's doing
    if (text[0] != '\0') {
        int dummy_prefix = str_lookup(" ", t->sorted_vocab, t->vocab_size);
        tokens[(*n_tokens)++] = dummy_prefix;
    }

    // Okay UTF-8 time. This will get messy. Here is the reference from Wikipedia:
    // Code point ↔ UTF-8 conversion
    // First code point	Last code point	Byte 1	Byte 2	Byte 3	Byte 4
    // U+0000	U+007F	    0xxxxxxx
    // U+0080	U+07FF	    110xxxxx	10xxxxxx
    // U+0800	U+FFFF	    1110xxxx	10xxxxxx	10xxxxxx
    // U+10000	U+10FFFF    11110xxx	10xxxxxx	10xxxxxx	10xxxxxx

    // process the raw (UTF-8) byte sequence of the input string
    for (char *c = text; *c != '\0'; c++) {

        // reset buffer if the current byte is ASCII or a leading byte
        // 0xC0 is 11000000, so (*c & 0xC0) keeps the first 2 bits and zeros the rest
        // 0x80 is 10000000
        // in UTF-8, all continuation bytes start with "10" in first two bits
        // so in English this is: "if this byte is not a continuation byte"
        if ((*c & 0xC0) != 0x80) {
            // this byte must be either a leading byte (11...) or an ASCII char (0x...)
            // => reset our location, as we're starting a new UTF-8 codepoint
            str_len = 0;
        }

        // append the current byte to the buffer
        str_buffer[str_len++] = *c; // ++ is post-increment, incremented after this line
        str_buffer[str_len] = '\0';

        // while the next character is a continuation byte, continue appending
        // but if there are too many of them, just stop to avoid overruning str_buffer size.
        if ((*(c+1) & 0xC0) == 0x80 && str_len < 4) {
            continue;
        }

        // ok c+1 is not a continuation byte, so we've read in a full codepoint
        int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);

        if (id != -1) {
            // we found this codepoint in vocab, add it as a token
            tokens[(*n_tokens)++] = id;
        } else {
            // byte_fallback encoding: just encode each byte as a token
            // +3 is here because the first 3 vocab elements are <unk>, <s>, </s>
            // so the individual bytes only start at index 3
            for (int i=0; i < str_len; i++) {
                tokens[(*n_tokens)++] = (unsigned char)str_buffer[i] + 3;
            }
        }
        str_len = 0; // protect against a sequence of stray UTF8 continuation bytes
    }

    // merge the best consecutive pair each iteration, according the scores in vocab_scores
    while (1) {
        float best_score = -1e10;
        int best_id = -1;
        int best_idx = -1;

        for (int i=0; i < (*n_tokens-1); i++) {
            // check if we can merge the pair (tokens[i], tokens[i+1])
            sprintf(str_buffer, "%s%s", t->vocab[tokens[i]], t->vocab[tokens[i+1]]);
            int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);
            if (id != -1 && t->vocab_scores[id] > best_score) {
                // this merge pair exists in vocab! record its score and position
                best_score = t->vocab_scores[id];
                best_id = id;
                best_idx = i;
            }
        }

        if (best_idx == -1) {
            break; // we couldn't find any more pairs to merge, so we're done
        }

        // merge the consecutive pair (best_idx, best_idx+1) into new token best_id
        tokens[best_idx] = best_id;
        // delete token at position best_idx+1, shift the entire sequence back 1
        for (int i = best_idx+1; i < (*n_tokens-1); i++) {
            tokens[i] = tokens[i+1];
        }
        (*n_tokens)--; // token length decreased
    }

    // add optional EOS (=2) token, if desired
    if (eos) tokens[(*n_tokens)++] = 2;

    free(str_buffer);
}

// ----------------------------------------------------------------------------
// The Sampler, which takes logits and returns a sampled token
// sampling can be done in a few ways: greedy argmax, sampling, top-p sampling

typedef struct {
    float prob;
    int index;
} ProbIndex; // struct used when sorting probabilities during top-p sampling

typedef struct {
    int vocab_size;
    ProbIndex* probindex; // buffer used in top-p sampling
    float temperature;
    float topp;
    unsigned long long rng_state;
} Sampler;

int sample_argmax(float* probabilities, int n) {
    // return the index that has the highest probability
    int max_i = 0;
    float max_p = probabilities[0];
    for (int i = 1; i < n; i++) {
        if (probabilities[i] > max_p) {
            max_i = i;
            max_p = probabilities[i];
        }
    }
    return max_i;
}

int sample_mult(float* probabilities, int n, float coin) {
    // sample index from probabilities (they must sum to 1!)
    // coin is a random number in [0, 1), usually from random_f32()
    float cdf = 0.0f;
    for (int i = 0; i < n; i++) {
        cdf += probabilities[i];
        if (coin < cdf) {
            return i;
        }
    }
    return n - 1; // in case of rounding errors
}

int compare(const void* a, const void* b) {
    ProbIndex* a_ = (ProbIndex*) a;
    ProbIndex* b_ = (ProbIndex*) b;
    if (a_->prob > b_->prob) return -1;
    if (a_->prob < b_->prob) return 1;
    return 0;
}

int sample_topp(float* probabilities, int n, float topp, ProbIndex* probindex, float coin) {
    // top-p sampling (or "nucleus sampling") samples from the smallest set of
    // tokens that exceed probability topp. This way we never sample tokens that
    // have very low probabilities and are less likely to go "off the rails".
    // coin is a random number in [0, 1), usually from random_f32()

    int n0 = 0;
    // quicksort indices in descending order of probabilities
    // values smaller than (1 - topp) / (n - 1) cannot be part of the result
    // so for efficiency we crop these out as candidates before sorting
    const float cutoff = (1.0f - topp) / (n - 1);
    for (int i = 0; i < n; i++) {
        if (probabilities[i] >= cutoff) {
            probindex[n0].index = i;
            probindex[n0].prob = probabilities[i];
            n0++;
        }
    }
    qsort(probindex, n0, sizeof(ProbIndex), compare);

    // truncate the list where cumulative probability exceeds topp
    float cumulative_prob = 0.0f;
    int last_idx = n0 - 1; // in case of rounding errors consider all elements
    for (int i = 0; i < n0; i++) {
        cumulative_prob += probindex[i].prob;
        if (cumulative_prob > topp) {
            last_idx = i;
            break; // we've exceeded topp by including last_idx
        }
    }

    // sample from the truncated list
    float r = coin * cumulative_prob;
    float cdf = 0.0f;
    for (int i = 0; i <= last_idx; i++) {
        cdf += probindex[i].prob;
        if (r < cdf) {
            return probindex[i].index;
        }
    }
    return probindex[last_idx].index; // in case of rounding errors
}

void build_sampler(Sampler* sampler, int vocab_size, float temperature, float topp, unsigned long long rng_seed) {
    sampler->vocab_size = vocab_size;
    sampler->temperature = temperature;
    sampler->topp = topp;
    sampler->rng_state = rng_seed;
    // buffer only used with nucleus sampling; may not need but it's ~small
    sampler->probindex = malloc(sampler->vocab_size * sizeof(ProbIndex));
}

void free_sampler(Sampler* sampler) {
    free(sampler->probindex);
}

unsigned int random_u32(unsigned long long *state) {
    // xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
    *state ^= *state >> 12;
    *state ^= *state << 25;
    *state ^= *state >> 27;
    return (*state * 0x2545F4914F6CDD1Dull) >> 32;
}
float random_f32(unsigned long long *state) { // random float32 in [0,1)
    return (random_u32(state) >> 8) / 16777216.0f;
}

int sample(Sampler* sampler, float* logits) {
    // sample the token given the logits and some hyperparameters
    int next;
    if (sampler->temperature == 0.0f) {
        // greedy argmax sampling: take the token with the highest probability
        next = sample_argmax(logits, sampler->vocab_size);
    } else {
        // apply the temperature to the logits
        for (int q=0; q<sampler->vocab_size; q++) { logits[q] /= sampler->temperature; }
        // apply softmax to the logits to get the probabilities for next token
        softmax(logits, sampler->vocab_size);
        // flip a (float) coin (this is our source of entropy for sampling)
        float coin = random_f32(&sampler->rng_state);
        // we sample from this distribution to get the next token
        if (sampler->topp <= 0 || sampler->topp >= 1) {
            // simply sample from the predicted probability distribution
            next = sample_mult(logits, sampler->vocab_size, coin);
        } else {
            // top-p (nucleus) sampling, clamping the least likely tokens to zero
            next = sample_topp(logits, sampler->vocab_size, sampler->topp, sampler->probindex, coin);
        }
    }
    return next;
}

// ----------------------------------------------------------------------------
// utilities: time

long time_in_ms() {
    // return time in milliseconds, for benchmarking the model speed
    struct timespec time;
    clock_gettime(CLOCK_REALTIME, &time);
    return time.tv_sec * 1000 + time.tv_nsec / 1000000;
}

// ----------------------------------------------------------------------------
// generation loop

void generate(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler, char *prompt, int steps) {
    char *empty_prompt = "";
    if (prompt == NULL) { prompt = empty_prompt; }

    // encode the (string) prompt into tokens sequence
    int num_prompt_tokens = 0;
    int* prompt_tokens = (int*)malloc((strlen(prompt)+3) * sizeof(int)); // +3 for '\0', ?BOS, ?EOS
    encode(tokenizer, prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
    if (num_prompt_tokens < 1) {
        fprintf(stderr, "something is wrong, expected at least 1 prompt token\n");
        exit(EXIT_FAILURE);
    }

    // start the main loop
    long start = 0;  // used to time our code, only initialized after first iteration
    int next;        // will store the next token in the sequence
    int token = prompt_tokens[0]; // kick off with the first token in the prompt
    int pos = 0;     // position in the sequence
    while (pos < steps) {

        // forward the transformer to get logits for the next token
        float* logits = forward(transformer, token, pos);

        // advance the state machine
        if (pos < num_prompt_tokens - 1) {
            // if we are still processing the input prompt, force the next prompt token
            next = prompt_tokens[pos + 1];
        } else {
            // otherwise sample the next token from the logits
            next = sample(sampler, logits);
        }
        pos++;

        // data-dependent terminating condition: the BOS (=1) token delimits sequences
        if (next == 1) { break; }

        // print the token as string, decode it with the Tokenizer object
        char* piece = decode(tokenizer, token, next);
        safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
        fflush(stdout);
        token = next;

        // init the timer here because the first iteration can be slower
        if (start == 0) { start = time_in_ms(); }
    }
    printf("\n");

    // report achieved tok/s (pos-1 because the timer starts after first iteration)
    if (pos > 1) {
        long end = time_in_ms();
        fprintf(stderr, "achieved tok/s: %f\n", (pos-1) / (double)(end-start)*1000);
    }

    free(prompt_tokens);
}

void read_stdin(const char* guide, char* buffer, size_t bufsize) {
    // read a line from stdin, up to but not including \n
    printf("%s", guide);
    if (fgets(buffer, bufsize, stdin) != NULL) {
        size_t len = strlen(buffer);
        if (len > 0 && buffer[len - 1] == '\n') {
            buffer[len - 1] = '\0'; // strip newline
        }
    }
}

// ----------------------------------------------------------------------------
// chat loop
// I manually inspected the tokens for a few chat conversations compared to
// python reference and that seemed ok, but this was not thoroughly tested and
// is not safely implemented, it's more a proof of concept atm.

void chat(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler,
          char *cli_user_prompt, char *cli_system_prompt, int steps) {

    // buffers for reading the system prompt and user prompt from stdin
    // you'll notice they are soomewhat haphazardly and unsafely set atm
    char system_prompt[512];
    char user_prompt[512];
    char rendered_prompt[1152];
    int num_prompt_tokens = 0;
    int* prompt_tokens = (int*)malloc(1152 * sizeof(int));
    int user_idx;

    // start the main loop
    int8_t user_turn = 1; // user starts
    int next;        // will store the next token in the sequence
    int token;       // stores the current token to feed into the transformer
    int prev_token;
    int pos = 0;     // position in the sequence
    while (pos < steps) {

        // when it is the user's turn to contribute tokens to the dialog...
        if (user_turn) {
            // get the (optional) system prompt at position 0
            if (pos == 0) {
                // at position 0, the user can also contribute a system prompt
                if (cli_system_prompt == NULL) {
                    // system prompt was not passed in, attempt to get it from stdin
                    read_stdin("Enter system prompt (optional): ", system_prompt, sizeof(system_prompt));
                } else {
                    // system prompt was passed in, use it
                    strcpy(system_prompt, cli_system_prompt);
                }
            }
            // get the user prompt
            if (pos == 0 && cli_user_prompt != NULL) {
                // user prompt for position 0 was passed in, use it
                strcpy(user_prompt, cli_user_prompt);
            } else {
                // otherwise get user prompt from stdin
                read_stdin("User: ", user_prompt, sizeof(user_prompt));
            }
            // render user/system prompts into the Llama 2 Chat schema
            if (pos == 0 && system_prompt[0] != '\0') {
                char system_template[] = "[INST] <<SYS>>\n%s\n<</SYS>>\n\n%s [/INST]";
                sprintf(rendered_prompt, system_template, system_prompt, user_prompt);
            } else {
                char user_template[] = "[INST] %s [/INST]";
                sprintf(rendered_prompt, user_template, user_prompt);
            }
            // encode the rendered prompt into tokens
            encode(tokenizer, rendered_prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
            user_idx = 0; // reset the user index
            user_turn = 0;
            printf("Assistant: ");
        }

        // determine the token to pass into the transformer next
        if (user_idx < num_prompt_tokens) {
            // if we are still processing the input prompt, force the next prompt token
            token = prompt_tokens[user_idx++];
        } else {
            // otherwise use the next token sampled from previous turn
            token = next;
        }
        // EOS (=2) token ends the Assistant turn
        if (token == 2) { user_turn = 1; }

        // forward the transformer to get logits for the next token
        float* logits = forward(transformer, token, pos);
        next = sample(sampler, logits);
        pos++;

        if (user_idx >= num_prompt_tokens && next != 2) {
            // the Assistant is responding, so print its output
            char* piece = decode(tokenizer, token, next);
            safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
            fflush(stdout);
        }
        if (next == 2) { printf("\n"); }
    }
    printf("\n");
    free(prompt_tokens);
}


// ----------------------------------------------------------------------------
// CLI, include only if not testing
#ifndef TESTING

void error_usage() {
    fprintf(stderr, "Usage:   run <checkpoint> [options]\n");
    fprintf(stderr, "Example: run model.bin -n 256 -i \"Once upon a time\"\n");
    fprintf(stderr, "Options:\n");
    fprintf(stderr, "  -t <float>  temperature in [0,inf], default 1.0\n");
    fprintf(stderr, "  -p <float>  p value in top-p (nucleus) sampling in [0,1] default 0.9\n");
    fprintf(stderr, "  -s <int>    random seed, default time(NULL)\n");
    fprintf(stderr, "  -n <int>    number of steps to run for, default 256. 0 = max_seq_len\n");
    fprintf(stderr, "  -i <string> input prompt\n");
    fprintf(stderr, "  -z <string> optional path to custom tokenizer\n");
    fprintf(stderr, "  -m <string> mode: generate|chat, default: generate\n");
    fprintf(stderr, "  -y <string> (optional) system prompt in chat mode\n");
    exit(EXIT_FAILURE);
}

int main(int argc, char *argv[]) {

    // default parameters
    char *checkpoint_path = NULL;  // e.g. out/model.bin
    char *tokenizer_path = "tokenizer.bin";
    float temperature = 1.0f;   // 0.0 = greedy deterministic. 1.0 = original. don't set higher
    float topp = 0.9f;          // top-p in nucleus sampling. 1.0 = off. 0.9 works well, but slower
    int steps = 256;            // number of steps to run for
    char *prompt = NULL;        // prompt string
    unsigned long long rng_seed = 0; // seed rng with time by default
    char *mode = "generate";    // generate|chat
    char *system_prompt = NULL; // the (optional) system prompt to use in chat mode

    // poor man's C argparse so we can override the defaults above from the command line
    if (argc >= 2) { checkpoint_path = argv[1]; } else { error_usage(); }
    for (int i = 2; i < argc; i+=2) {
        // do some basic validation
        if (i + 1 >= argc) { error_usage(); } // must have arg after flag
        if (argv[i][0] != '-') { error_usage(); } // must start with dash
        if (strlen(argv[i]) != 2) { error_usage(); } // must be -x (one dash, one letter)
        // read in the args
        if (argv[i][1] == 't') { temperature = atof(argv[i + 1]); }
        else if (argv[i][1] == 'p') { topp = atof(argv[i + 1]); }
        else if (argv[i][1] == 's') { rng_seed = atoi(argv[i + 1]); }
        else if (argv[i][1] == 'n') { steps = atoi(argv[i + 1]); }
        else if (argv[i][1] == 'i') { prompt = argv[i + 1]; }
        else if (argv[i][1] == 'z') { tokenizer_path = argv[i + 1]; }
        else if (argv[i][1] == 'm') { mode = argv[i + 1]; }
        else if (argv[i][1] == 'y') { system_prompt = argv[i + 1]; }
        else { error_usage(); }
    }

    // parameter validation/overrides
    if (rng_seed <= 0) rng_seed = (unsigned int)time(NULL);
    if (temperature < 0.0) temperature = 0.0;
    if (topp < 0.0 || 1.0 < topp) topp = 0.9;
    if (steps < 0) steps = 0;

    // build the Transformer via the model .bin file
    Transformer transformer;
    build_transformer(&transformer, checkpoint_path);
    if (steps == 0 || steps > transformer.config.seq_len) steps = transformer.config.seq_len; // override to ~max length

    // build the Tokenizer via the tokenizer .bin file
    Tokenizer tokenizer;
    build_tokenizer(&tokenizer, tokenizer_path, transformer.config.vocab_size);

    // build the Sampler
    Sampler sampler;
    build_sampler(&sampler, transformer.config.vocab_size, temperature, topp, rng_seed);

    // run!
    if (strcmp(mode, "generate") == 0) {
        generate(&transformer, &tokenizer, &sampler, prompt, steps);
    } else if (strcmp(mode, "chat") == 0) {
        chat(&transformer, &tokenizer, &sampler, prompt, system_prompt, steps);
    } else {
        fprintf(stderr, "unknown mode: %s\n", mode);
        error_usage();
    }

    // memory and file handles cleanup
    free_sampler(&sampler);
    free_tokenizer(&tokenizer);
    free_transformer(&transformer);
    return 0;
}
#endif

5.1.3 生成可执行文件

gcc -o run run.c

运行后会在根目录下生成run.exe文件。

5.2 模型推理

./run out/model.bin

输出如下：

Once upon a time, there was a little girl named Lily. She loved to play in the garden and watch the insects crawl around. One day, while she was playing, she saw a butterfly flying around. “Hello butterfly, what are you doing?” Lily said, feeling grateful. “I am waiting for my mom to come back to check on her,” the butterfly replied. Lily watched as the butterfly flew away, feeling proud that she had made it happen. From that day on, she loved looking at insects and watching them grow and become happy.
achieved tok/s: 24.576110

还可以指定参数运行：

./run .\out\model.bin -i "One day, Lily met a Shoggoth"

也可以从Huggingface网站下载其他模型试试：

wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.bin
./run stories15M.bin

wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories42M.bin
./run stories42M.bin

备注：清华大学开源软件镜像站

6 结束语

大功告成！单卡RTX4070刚好能跑起来。
Enjoy it！