llama2c（4）之forward、sample、decode

1、forward

float* logits = forward(transformer, token, pos);
输入transformer的参数，当前token，pos位置，预测出下一个token的预测值（用矩阵乘，加减乘除等运算构成Transformer）
其中，logits如下：
s->logits = calloc(p->vocab_size, sizeof(float));
matmul(s->logits, &s->xq, w->wcls, dim, p->vocab_size);
根据以上两行代码，和matmul的定义matmul函数的定义，输出的s->logits维度是[1,p->vocab_size]

对应vocab每个字符串的概率分布情况

gdb) p *logits@1000
$15 = {-0.283571005, 3.44877911, -0.578277588, -3.24091816, -1.85795152, 2.61188054, -0.770998061, 0.366253316, -0.637891531, 0.122880608, 2.0521276, 0.259968579, 0.553953588, 1.23023224, -1.90220821, 0.791390121, -0.279410094, -2.03433132, 0.736696005, -2.83315516, 0.430814654, -0.45484668, -0.296925813, -0.776587725, -0.373722374, -1.41853309, 0.44897157, 0.298399687, -2.28996897, -0.504646838, -0.219529897, 0.334682822, 0.359610289, 1.333992, -0.0392727256, -0.277485281, -0.281440586, -0.278330177, -0.279631168, -0.275823981, -0.273261875, -0.281633765, -0.280521065, -0.279279858, -0.277830899, -0.275540143, -0.278773159, -0.285891086, -0.275212795, -0.27603671, -0.276746958, -0.281391174, -0.27630195, -0.278620541, -0.281585068, -0.277181506, -0.279754519, -0.276037633, -0.278509229, -0.278621584, -0.271104455, -0.280266523, -0.279526323, -0.280170411, -0.277653664, -0.28433004, -0.275049627, -0.280639797, -0.27556017, -0.279702693, -0.286844194, -0.277686894, -0.278450489, -0.28413251, -0.279598236, -0.273824662, -0.276941836, -0.279240847, -0.281096309, -0.275031894, -0.282162875, -0.282587916, -0.279308707, -0.279815942, -0.280733585, -0.278700113, -0.275241196, -0.273779333, -0.280413181, -0.277753592, 
--Type <RET> for more, q to quit, c to continue without paging--

// attention rmsnorm
rmsnorm(s->xb, x, w->rms_att_weight + l*dim, dim);
// qkv matmuls for this position
quantize(&s->xq, s->xb, dim);
matmul(s->q, &s->xq, w->wq + l, dim, dim);

(gdb) ptype s->xb
type = float *

量化是输入是确保与权重一样的数据类型

2、sample

2.1 未进入

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

**确定next，**如果还在input prompt，那么下一个token就是next；不是，才用sample得出next
即执行

next = prompt_tokens[pos + 1];

得

(gdb) p pos
$10 = 0
(gdb) p next
$11 = 15043  //Hello

2.2 进入

根据参数进行采样，生成下一个词的token。

定义：
int sample(Sampler* sampler, float* logits)
(gdb) p *logits
$20 = 0.657589614
(gdb) p *sampler
$1 = {vocab_size = 32000, probindex = 0x7f12efe3b010, temperature = 1, topp = 0.899999976, rng_state = 1710049046}
`temperature`：控制文本生成随机性的参数，0.0意味着最确定（只选最高概率的词），1.0为原始概率分布，值越高生成结果越多样但可能偏离训练数据趋势。`topp`：在核抽样技术中，决定词汇选择集合的阈值，如设为0.9，则仅考虑累积概率最高的那部分词汇。较低的topp值有助于生成更连贯、高质量文本，但计算上较慢。`rng_seed`：初始化随机数生成器的种子，默认用当前时间，确保每次运行有不同随机性。设定特定种子可复现相同的随机序列，对生成一致性文本结果有用。

部分代码解释：

1. temperature=0.0

sampler->temperature == 0.0f
next = sample_argmax(logits, sampler->vocab_size);

调用sample_argmax选取返回概率最高的那个索引

2. temperature！= 0.0
   每个logits[q]除以sampler->temperature，并通过softmax函数中转化为更符合当前温度设置的概率分布。
   2）_1 当sampler->topp <= 0 或者 sampler->topp >= 1时，用sample_mult函数

调用

 next = sample_mult(logits, sampler->vocab_size, coin);

// sample index from probabilities (they must sum to 1!)
// coin is a random number in [0, 1), usually from random_f32()
定义

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) {    //遍历累加，并同时判断cdf的是否大于coin，有，就返回ireturn i;}}return n - 1; // in case of rounding errors  如果没有就返回n-1
}

2）_2 其他，top-p策略
调用

next = sample_topp(logits, sampler->vocab_size, sampler->topp, sampler->probindex, coin);

参数意义：

$1 = {vocab_size = 32000, probindex = 0x7f12efe3b010, temperature = 1, topp = 0.899999976, rng_state = 1710049046}
float topp: 采样阈值，通常在(0,1)之间，表示我们只考虑累积概率超过这个阈值的那一部分词汇。
ProbIndex* probindex: 一个结构体类型的数组，用于存储经过筛选后的索引及其对应概率。

定义

int sample_topp(float* probabilities, int n, float topp, ProbIndex* probindex, float coin)

**S1：**只保留概率大于等于 (1 - topp) / (n - 1) 的词汇，并将其对应的索引和概率存入 probindex 结构体数组。并按降序排序

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);

S2：和上面sample_mult函数语言，这儿只是对筛选后的probindex的里面概率进行累加，如果大于了topp，返回idx

  // truncate the list where cumulative probability exceeds toppfloat cumulative_prob = 0.0f;int last_idx = n0 - 1; // in case of rounding errors consider all elementsfor (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}}

S3：根据coin和筛选后的累计概率决定采样那个词汇， return probindex[i].index

  // sample from the truncated listfloat 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
}

3、decode

token=1，next=15043

调用
char* piece = decode(tokenizer, token, next);
定义
char* decode(Tokenizer* t, int prev_token, int token)
{char *piece = t->vocab[token];   //Hello// 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 byteunsigned char byte_val;if (sscanf(piece, "<0x%02hhX>", &byte_val) == 1) {piece = (char*)t->byte_pieces + byte_val * 2;}return piece;
}
(gdb) p piece
$17 = 0x55ae4f286661 "Hello"