Andrej Karpathy
682 lines
26 KiB
C
682 lines
26 KiB
C
/*
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Inference for Llama-2 Transformer model in pure C.
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Example compile: (see README for more details)
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$ gcc -O3 -o run run.c -lm
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Then run with:
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$ ./run
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*/
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#include <stdio.h>
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#include <stdlib.h>
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#include <time.h>
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#include <math.h>
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#include <string.h>
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#include <fcntl.h>
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#if defined _WIN32
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#include "win.h"
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#else
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#include <unistd.h>
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#include <sys/mman.h>
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#endif
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// ----------------------------------------------------------------------------
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// Transformer and RunState structs, and related memory management
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typedef struct {
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int dim; // transformer dimension
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int hidden_dim; // for ffn layers
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int n_layers; // number of layers
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int n_heads; // number of query heads
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int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
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int vocab_size; // vocabulary size, usually 256 (byte-level)
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int seq_len; // max sequence length
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} Config;
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typedef struct {
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// token embedding table
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float* token_embedding_table; // (vocab_size, dim)
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// weights for rmsnorms
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float* rms_att_weight; // (layer, dim) rmsnorm weights
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float* rms_ffn_weight; // (layer, dim)
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// weights for matmuls. note dim == n_heads * head_size
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float* wq; // (layer, dim, n_heads * head_size)
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float* wk; // (layer, dim, n_kv_heads * head_size)
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float* wv; // (layer, dim, n_kv_heads * head_size)
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float* wo; // (layer, n_heads * head_size, dim)
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// weights for ffn
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float* w1; // (layer, hidden_dim, dim)
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float* w2; // (layer, dim, hidden_dim)
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float* w3; // (layer, hidden_dim, dim)
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// final rmsnorm
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float* rms_final_weight; // (dim,)
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// freq_cis for RoPE relatively positional embeddings
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float* freq_cis_real; // (seq_len, head_size/2)
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float* freq_cis_imag; // (seq_len, head_size/2)
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// (optional) classifier weights for the logits, on the last layer
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float* wcls;
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} TransformerWeights;
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typedef struct {
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float prob;
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int index;
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} ProbIndex; // struct used when sorting probabilities during top-p sampling
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typedef struct {
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// current wave of activations
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float *x; // activation at current time stamp (dim,)
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float *xb; // same, but inside a residual branch (dim,)
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float *xb2; // an additional buffer just for convenience (dim,)
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float *hb; // buffer for hidden dimension in the ffn (hidden_dim,)
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float *hb2; // buffer for hidden dimension in the ffn (hidden_dim,)
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float *q; // query (dim,)
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float *k; // key (dim,)
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float *v; // value (dim,)
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float *att; // buffer for scores/attention values (n_heads, seq_len)
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float *logits; // output logits
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ProbIndex *probindex; // buffer used in top-p sampling
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// kv cache
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float* key_cache; // (layer, seq_len, dim)
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float* value_cache; // (layer, seq_len, dim)
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} RunState;
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void malloc_run_state(RunState* s, Config* p) {
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// we calloc instead of malloc to keep valgrind happy
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int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
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s->x = calloc(p->dim, sizeof(float));
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s->xb = calloc(p->dim, sizeof(float));
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s->xb2 = calloc(p->dim, sizeof(float));
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s->hb = calloc(p->hidden_dim, sizeof(float));
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s->hb2 = calloc(p->hidden_dim, sizeof(float));
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s->q = calloc(p->dim, sizeof(float));
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s->k = calloc(kv_dim, sizeof(float));
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s->v = calloc(kv_dim, sizeof(float));
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s->att = calloc(p->n_heads * p->seq_len, sizeof(float));
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s->logits = calloc(p->vocab_size, sizeof(float));
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s->probindex = calloc(p->vocab_size, sizeof(ProbIndex));
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s->key_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
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s->value_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
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// ensure all mallocs went fine
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if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q
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|| !s->k || !s->v || !s->att || !s->logits || !s->key_cache
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|| !s->value_cache || !s->probindex) {
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fprintf(stderr, "malloc failed!\n");
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exit(EXIT_FAILURE);
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}
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}
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void free_run_state(RunState* s) {
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free(s->x);
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free(s->xb);
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free(s->xb2);
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free(s->hb);
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free(s->hb2);
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free(s->q);
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free(s->k);
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free(s->v);
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free(s->att);
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free(s->logits);
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free(s->probindex);
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free(s->key_cache);
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free(s->value_cache);
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}
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// ----------------------------------------------------------------------------
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// initialization: read from checkpoint
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void checkpoint_init_weights(TransformerWeights *w, Config* p, float* f, int shared_weights) {
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int head_size = p->dim / p->n_heads;
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float* ptr = f;
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w->token_embedding_table = ptr;
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ptr += p->vocab_size * p->dim;
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w->rms_att_weight = ptr;
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ptr += p->n_layers * p->dim;
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w->wq = ptr;
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ptr += p->n_layers * p->dim * (p->n_heads * head_size);
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w->wk = ptr;
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ptr += p->n_layers * p->dim * (p->n_kv_heads * head_size);
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w->wv = ptr;
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ptr += p->n_layers * p->dim * (p->n_kv_heads * head_size);
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w->wo = ptr;
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ptr += p->n_layers * (p->n_heads * head_size) * p->dim;
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w->rms_ffn_weight = ptr;
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ptr += p->n_layers * p->dim;
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w->w1 = ptr;
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ptr += p->n_layers * p->dim * p->hidden_dim;
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w->w2 = ptr;
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ptr += p->n_layers * p->hidden_dim * p->dim;
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w->w3 = ptr;
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ptr += p->n_layers * p->dim * p->hidden_dim;
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w->rms_final_weight = ptr;
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ptr += p->dim;
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w->freq_cis_real = ptr;
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ptr += p->seq_len * head_size / 2;
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w->freq_cis_imag = ptr;
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ptr += p->seq_len * head_size / 2;
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w->wcls = shared_weights ? w->token_embedding_table : ptr;
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}
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// ----------------------------------------------------------------------------
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// neural net blocks
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void rmsnorm(float* o, float* x, float* weight, int size) {
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// calculate sum of squares
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float ss = 0.0f;
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for (int j = 0; j < size; j++) {
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ss += x[j] * x[j];
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}
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ss /= size;
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ss += 1e-5f;
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ss = 1.0f / sqrtf(ss);
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// normalize and scale
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for (int j = 0; j < size; j++) {
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o[j] = weight[j] * (ss * x[j]);
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}
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}
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void softmax(float* x, int size) {
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// find max value (for numerical stability)
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float max_val = x[0];
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for (int i = 1; i < size; i++) {
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if (x[i] > max_val) {
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max_val = x[i];
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}
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}
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// exp and sum
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float sum = 0.0f;
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for (int i = 0; i < size; i++) {
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x[i] = expf(x[i] - max_val);
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sum += x[i];
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}
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// normalize
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for (int i = 0; i < size; i++) {
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x[i] /= sum;
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}
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}
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void matmul(float* xout, float* x, float* w, int n, int d) {
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// W (d,n) @ x (n,) -> xout (d,)
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// by far the most amount of time is spent inside this little function
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int i;
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#pragma omp parallel for private(i)
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for (i = 0; i < d; i++) {
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float val = 0.0f;
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for (int j = 0; j < n; j++) {
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val += w[i * n + j] * x[j];
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}
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xout[i] = val;
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}
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}
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void transformer(int token, int pos, Config* p, RunState* s, TransformerWeights* w) {
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// a few convenience variables
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float *x = s->x;
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int dim = p->dim;
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int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
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int kv_mul = p->n_heads / p->n_kv_heads; // integer multiplier of the kv sharing in multiquery
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int hidden_dim = p->hidden_dim;
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int head_size = dim / p->n_heads;
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// copy the token embedding into x
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float* content_row = &(w->token_embedding_table[token * dim]);
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memcpy(x, content_row, dim*sizeof(*x));
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// pluck out the "pos" row of freq_cis_real and freq_cis_imag
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float* freq_cis_real_row = w->freq_cis_real + pos * head_size / 2;
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float* freq_cis_imag_row = w->freq_cis_imag + pos * head_size / 2;
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// forward all the layers
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for(int l = 0; l < p->n_layers; l++) {
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// attention rmsnorm
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rmsnorm(s->xb, x, w->rms_att_weight + l*dim, dim);
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// qkv matmuls for this position
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matmul(s->q, s->xb, w->wq + l*dim*dim, dim, dim);
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matmul(s->k, s->xb, w->wk + l*dim*kv_dim, dim, kv_dim);
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matmul(s->v, s->xb, w->wv + l*dim*kv_dim, dim, kv_dim);
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// RoPE relative positional encoding: complex-valued rotate q and k by freq_cis in each head
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for (int i = 0; i < dim; i+=2) {
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float q0 = s->q[i];
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float q1 = s->q[i+1];
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float fcr = freq_cis_real_row[(i % head_size) / 2];
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float fci = freq_cis_imag_row[(i % head_size) / 2];
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s->q[i] = q0 * fcr - q1 * fci;
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s->q[i+1] = q0 * fci + q1 * fcr;
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}
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for (int i = 0; i < kv_dim; i+=2) {
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float k0 = s->k[i];
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float k1 = s->k[i+1];
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float fcr = freq_cis_real_row[(i % head_size) / 2];
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float fci = freq_cis_imag_row[(i % head_size) / 2];
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s->k[i] = k0 * fcr - k1 * fci;
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s->k[i+1] = k0 * fci + k1 * fcr;
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}
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// save key,value at this time step (pos) to our kv cache
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int loff = l * p->seq_len * kv_dim; // kv cache layer offset for convenience
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float* key_cache_row = s->key_cache + loff + pos * kv_dim;
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float* value_cache_row = s->value_cache + loff + pos * kv_dim;
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memcpy(key_cache_row, s->k, kv_dim * sizeof(*key_cache_row));
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memcpy(value_cache_row, s->v, kv_dim * sizeof(*value_cache_row));
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// multihead attention. iterate over all heads
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int h;
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#pragma omp parallel for private(h)
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for (h = 0; h < p->n_heads; h++) {
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// get the query vector for this head
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float* q = s->q + h * head_size;
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// attention scores for this head
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float* att = s->att + h * p->seq_len;
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// iterate over all timesteps, including the current one
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for (int t = 0; t <= pos; t++) {
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// get the key vector for this head and at this timestep
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float* k = s->key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
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// calculate the attention score as the dot product of q and k
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float score = 0.0f;
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for (int i = 0; i < head_size; i++) {
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score += q[i] * k[i];
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}
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score /= sqrtf(head_size);
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// save the score to the attention buffer
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att[t] = score;
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}
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// softmax the scores to get attention weights, from 0..pos inclusively
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softmax(att, pos + 1);
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// weighted sum of the values, store back into xb
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float* xb = s->xb + h * head_size;
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memset(xb, 0, head_size * sizeof(float));
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for (int t = 0; t <= pos; t++) {
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// get the value vector for this head and at this timestep
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float* v = s->value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
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// get the attention weight for this timestep
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float a = att[t];
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// accumulate the weighted value into xb
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for (int i = 0; i < head_size; i++) {
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xb[i] += a * v[i];
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}
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}
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}
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// final matmul to get the output of the attention
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matmul(s->xb2, s->xb, w->wo + l*dim*dim, dim, dim);
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// residual connection back into x
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for (int i = 0; i < dim; i++) {
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x[i] += s->xb2[i];
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}
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// ffn rmsnorm
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rmsnorm(s->xb, x, w->rms_ffn_weight + l*dim, dim);
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// Now for FFN in PyTorch we have: self.w2(F.silu(self.w1(x)) * self.w3(x))
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// first calculate self.w1(x) and self.w3(x)
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matmul(s->hb, s->xb, w->w1 + l*dim*hidden_dim, dim, hidden_dim);
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matmul(s->hb2, s->xb, w->w3 + l*dim*hidden_dim, dim, hidden_dim);
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// F.silu; silu(x)=x*σ(x),where σ(x) is the logistic sigmoid
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for (int i = 0; i < hidden_dim; i++) {
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s->hb[i] = s->hb[i] * (1.0f / (1.0f + expf(-s->hb[i])));
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}
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// elementwise multiply with w3(x)
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for (int i = 0; i < hidden_dim; i++) {
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s->hb[i] = s->hb[i] * s->hb2[i];
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}
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// final matmul to get the output of the ffn
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matmul(s->xb, s->hb, w->w2 + l*dim*hidden_dim, hidden_dim, dim);
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// residual connection
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for (int i = 0; i < dim; i++) {
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x[i] += s->xb[i];
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}
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}
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// final rmsnorm
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rmsnorm(x, x, w->rms_final_weight, dim);
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// classifier into logits
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matmul(s->logits, x, w->wcls, p->dim, p->vocab_size);
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}
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// ----------------------------------------------------------------------------
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// byte pair encoding (BPE) tokenizer, encodes strings into tokens so we can prompt
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int str_lookup(char *str, char **vocab, int vocab_size) {
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// find the first perfect match for str in vocab, return its index or -1 if not found
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for (int i = 0; i < vocab_size; i++) {
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if (strcmp(str, vocab[i]) == 0) {
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return i;
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}
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}
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return -1;
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}
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void bpe_encode(char *text, char **vocab, float *vocab_scores, int vocab_size, unsigned int max_token_length, int *tokens, int *n_tokens) {
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// a temporary buffer to merge two consecutive tokens
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char* str_buffer = malloc((max_token_length*2+1) * sizeof(char)); // *2 for concat, +1 for null terminator
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// first encode every individual byte in the input string
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*n_tokens = 0; // the number of tokens
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for (char *c = text; *c != '\0'; c++) {
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sprintf(str_buffer, "%c", *c);
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int id = str_lookup(str_buffer, vocab, vocab_size);
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if (id == -1) { fprintf(stderr, "not good\n"); exit(EXIT_FAILURE); }
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tokens[*n_tokens] = id;
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(*n_tokens)++;
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}
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// merge the best consecutive pair each iteration, according the scores in vocab_scores
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while (1) {
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float best_score = -1e10;
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int best_id = -1;
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int best_idx = -1;
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for (int i=0; i < (*n_tokens-1); i++) {
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// check if we can merge the pair (tokens[i], tokens[i+1])
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sprintf(str_buffer, "%s%s", vocab[tokens[i]], vocab[tokens[i+1]]);
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int id = str_lookup(str_buffer, vocab, vocab_size);
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if (id != -1 && vocab_scores[id] > best_score) {
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// this merge pair exists in vocab! record its score and position
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best_score = vocab_scores[id];
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best_id = id;
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best_idx = i;
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}
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}
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if (best_idx == -1) {
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break; // we couldn't find any more pairs to merge, so we're done
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}
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// merge the consecutive pair (best_idx, best_idx+1) into new token best_id
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tokens[best_idx] = best_id;
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// delete token at position best_idx+1, shift the entire sequence back 1
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for (int i = best_idx+1; i < (*n_tokens-1); i++) {
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tokens[i] = tokens[i+1];
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}
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(*n_tokens)--; // token length decreased
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}
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free(str_buffer);
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}
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// ----------------------------------------------------------------------------
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// utilities: time / rng
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long time_in_ms() {
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// return time in milliseconds, for benchmarking the model speed
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struct timespec time;
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clock_gettime(CLOCK_REALTIME, &time);
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return time.tv_sec * 1000 + time.tv_nsec / 1000000;
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}
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unsigned long long rng_seed;
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unsigned int random_u32() {
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// xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
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rng_seed ^= rng_seed >> 12;
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rng_seed ^= rng_seed << 25;
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rng_seed ^= rng_seed >> 27;
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return (rng_seed * 0x2545F4914F6CDD1Dull) >> 32;
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}
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float random_f32() { // random float32 in [0,1)
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return (random_u32() >> 8) / 16777216.0f;
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}
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// ----------------------------------------------------------------------------
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// sampling can be done in a few ways: greedy argmax, sampling, top-p sampling
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int argmax(float* probabilities, int n) {
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// return the index that has the highest probability
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int max_i = 0;
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float max_p = probabilities[0];
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for (int i = 1; i < n; i++) {
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if (probabilities[i] > max_p) {
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max_i = i;
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max_p = probabilities[i];
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}
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}
|
||
return max_i;
|
||
}
|
||
|
||
int sample(float* probabilities, int n) {
|
||
// sample index from probabilities (they must sum to 1!)
|
||
float r = random_f32();
|
||
float cdf = 0.0f;
|
||
for (int i = 0; i < n; i++) {
|
||
cdf += probabilities[i];
|
||
if (r < 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) {
|
||
// 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".
|
||
|
||
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 = random_f32() * 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
|
||
}
|
||
|
||
|
||
// ----------------------------------------------------------------------------
|
||
// int main
|
||
|
||
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, default 1.0\n");
|
||
fprintf(stderr, " -p <float> p value in top-p (nucleus) sampling. 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");
|
||
exit(EXIT_FAILURE);
|
||
}
|
||
|
||
int main(int argc, char *argv[]) {
|
||
|
||
// default inits
|
||
char *checkpoint = NULL; // e.g. out/model.bin
|
||
char *tokenizer = "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
|
||
rng_seed = 0; // seed rng with time by default
|
||
int steps = 256; // number of steps to run for
|
||
char *prompt = NULL; // prompt string
|
||
|
||
// poor man's C argparse so we can override the defaults above from the command line
|
||
if (argc >= 2) { checkpoint = 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 = argv[i + 1]; }
|
||
else { error_usage(); }
|
||
}
|
||
if(rng_seed == 0) { rng_seed = (unsigned int)time(NULL);}
|
||
|
||
// read in the model.bin file
|
||
Config config;
|
||
TransformerWeights weights;
|
||
int fd = 0; // file descriptor for memory mapping
|
||
float* data = NULL; // memory mapped data pointer
|
||
ssize_t file_size; // size of the checkpoint file in bytes
|
||
{
|
||
FILE *file = fopen(checkpoint, "rb");
|
||
if (!file) { fprintf(stderr, "Couldn't open file %s\n", checkpoint); return 1; }
|
||
// read in the config header
|
||
if (fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
|
||
// 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);
|
||
// memory map the Transformer weights into the data pointer
|
||
fd = open(checkpoint, O_RDONLY); // open in read only mode
|
||
if (fd == -1) { fprintf(stderr, "open failed!\n"); return 1; }
|
||
data = mmap(NULL, file_size, PROT_READ, MAP_PRIVATE, fd, 0);
|
||
if (data == MAP_FAILED) { fprintf(stderr, "mmap failed!\n"); return 1; }
|
||
float* weights_ptr = data + sizeof(Config)/sizeof(float);
|
||
checkpoint_init_weights(&weights, &config, weights_ptr, shared_weights);
|
||
}
|
||
// right now we cannot run for more than config.seq_len steps
|
||
if (steps <= 0 || steps > config.seq_len) { steps = config.seq_len; }
|
||
|
||
// read in the tokenizer .bin file
|
||
char** vocab = (char**)malloc(config.vocab_size * sizeof(char*));
|
||
float* vocab_scores = (float*)malloc(config.vocab_size * sizeof(float));
|
||
unsigned int max_token_length;
|
||
{
|
||
FILE *file = fopen(tokenizer, "rb");
|
||
if (!file) { fprintf(stderr, "couldn't load %s\n", tokenizer); return 1; }
|
||
if (fread(&max_token_length, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); return 1; }
|
||
int len;
|
||
for (int i = 0; i < config.vocab_size; i++) {
|
||
if (fread(vocab_scores + i, sizeof(float), 1, file) != 1) { fprintf(stderr, "failed read\n"); return 1;}
|
||
if (fread(&len, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); return 1; }
|
||
vocab[i] = (char *)malloc(len + 1);
|
||
if (fread(vocab[i], len, 1, file) != 1) { fprintf(stderr, "failed read\n"); return 1; }
|
||
vocab[i][len] = '\0'; // add the string terminating token
|
||
}
|
||
fclose(file);
|
||
}
|
||
|
||
// create and init the application RunState
|
||
RunState state;
|
||
malloc_run_state(&state, &config);
|
||
|
||
// process the prompt, if any
|
||
int *prompt_tokens = NULL;
|
||
int num_prompt_tokens = 0;
|
||
if (prompt != NULL) {
|
||
prompt_tokens = (int*)malloc(strlen(prompt) * sizeof(int));
|
||
bpe_encode(prompt, vocab, vocab_scores, config.vocab_size, max_token_length, prompt_tokens, &num_prompt_tokens);
|
||
}
|
||
|
||
// 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 = 1; // init with token 1 (=BOS), as done in Llama-2 sentencepiece tokenizer
|
||
int pos = 0; // position in the sequence
|
||
while (pos < steps) {
|
||
|
||
// forward the transformer to get logits for the next token
|
||
transformer(token, pos, &config, &state, &weights);
|
||
|
||
// advance the state state machine
|
||
if(pos < num_prompt_tokens) {
|
||
// if we are still processing the input prompt, force the next prompt token
|
||
next = prompt_tokens[pos];
|
||
} else {
|
||
// sample the next token
|
||
if (temperature == 0.0f) {
|
||
// greedy argmax sampling: take the token with the highest probability
|
||
next = argmax(state.logits, config.vocab_size);
|
||
} else {
|
||
// apply the temperature to the logits
|
||
for (int q=0; q<config.vocab_size; q++) { state.logits[q] /= temperature; }
|
||
// apply softmax to the logits to get the probabilities for next token
|
||
softmax(state.logits, config.vocab_size);
|
||
// we sample from this distribution to get the next token
|
||
if (topp <= 0 || topp >= 1) {
|
||
// simply sample from the predicted probability distribution
|
||
next = sample(state.logits, config.vocab_size);
|
||
} else {
|
||
// top-p (nucleus) sampling, clamping the least likely tokens to zero
|
||
next = sample_topp(state.logits, config.vocab_size, topp, state.probindex);
|
||
}
|
||
}
|
||
}
|
||
pos++;
|
||
|
||
// data-dependent terminating condition: the BOS (1) token delimits sequences
|
||
if (next == 1) { break; }
|
||
|
||
// following BOS (1) token, sentencepiece decoder strips any leading whitespace (see PR #89)
|
||
char *token_str = (token == 1 && vocab[next][0] == ' ') ? vocab[next]+1 : vocab[next];
|
||
printf("%s", token_str);
|
||
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);
|
||
}
|
||
|
||
// memory and file handles cleanup
|
||
free_run_state(&state);
|
||
for (int i = 0; i < config.vocab_size; i++) { free(vocab[i]); }
|
||
free(vocab);
|
||
free(vocab_scores);
|
||
if (prompt_tokens != NULL) free(prompt_tokens);
|
||
if (data != MAP_FAILED) munmap(data, file_size);
|
||
if (fd != -1) close(fd);
|
||
return 0;
|
||
}
|