Egon Ruiter
932 lines
38 KiB
C
932 lines
38 KiB
C
/* Motore di inferenza OLMoE in C puro, con EXPERT-STREAMING dal disco.
|
|
* Porting del motore Python (engine.py). Obiettivo Stadio A: produrre gli STESSI
|
|
* token id del riferimento (ref.json) -> valida il core prima di scalare a GLM-5.2.
|
|
*
|
|
* Densa (embed, attn, router, norme, lm_head) residente in RAM (float32).
|
|
* Expert letti dal disco on-demand via pread+fadvise(DONTNEED), cache LRU per-layer.
|
|
* Matmul multi-thread con OpenMP (niente BLAS).
|
|
*
|
|
* ENV VARS:
|
|
* PILOT=0/1/2 : 0=no prefetch, 1=1-layer lookahead, 2=2-layer lookahead [IMPROVEMENT 1]
|
|
* HOT=N : pin top-N hot experts per layer permanently (never evict) [IMPROVEMENT 2]
|
|
* WARMUP=N : tokens before hot pinning activates (default 5) [IMPROVEMENT 2]
|
|
* REBAL=N : rebalance cache per-layer every N tokens (0=off) [IMPROVEMENT 3]
|
|
* WIDE=N : prefetch top-K*N candidates (default 1, try 2 or 3) [IMPROVEMENT 4]
|
|
* (expert queue is sorted by eid for SSD locality) [IMPROVEMENT 5]
|
|
*/
|
|
#define _GNU_SOURCE
|
|
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
#include <math.h>
|
|
#include <time.h>
|
|
#include <pthread.h>
|
|
#if defined(__APPLE__) || defined(__linux__) || defined(__FreeBSD__)
|
|
#include <sys/resource.h>
|
|
#include <unistd.h>
|
|
#endif
|
|
#include "st.h"
|
|
|
|
#ifdef _WIN32
|
|
#include <windows.h>
|
|
#define sleep_ms(ms) Sleep(ms)
|
|
#else
|
|
#define sleep_ms(ms) usleep((ms) * 1000)
|
|
#endif
|
|
|
|
|
|
|
|
/* ---------- config ---------- */
|
|
typedef struct {
|
|
int hidden, n_layers, n_heads, n_kv_heads, head_dim;
|
|
int n_experts, topk, inter, vocab;
|
|
float theta, eps; int norm_topk;
|
|
} Cfg;
|
|
|
|
/* ---------- pesi densi per-layer ---------- */
|
|
typedef struct {
|
|
float *in_ln, *post_ln, *q, *k, *v, *o, *qn, *kn, *gate;
|
|
} Layer;
|
|
|
|
/* ---------- cache LRU degli expert (pesi QUANTIZZATI) ----------
|
|
* Ogni weight [out,in] tenuto come int8 (per-riga) + scala float per riga.
|
|
* Cosi' la RAM-cache scende da 4 byte/param (f32) a 1 byte/param: e' il
|
|
* meccanismo che fa stare GLM-5.2 nei 15 GB. dequant-on-use nel matmul. */
|
|
/* IMPROVEMENT 2: pinned=1 means this slot is never evicted (hot expert). */
|
|
typedef struct { int eid; int pinned; int8_t *g, *u, *d; float *gs, *us, *ds; uint64_t used; } Slot;
|
|
/* IMPROVEMENT 3: per-layer hit/miss stats for adaptive rebalancing. */
|
|
typedef struct { Slot *slots; int n, cap; uint64_t layer_hits, layer_miss; } LCache;
|
|
|
|
typedef struct {
|
|
Cfg c;
|
|
shards S;
|
|
int quant_bits;
|
|
float *embed, *lm_head, *final_norm;
|
|
Layer *L;
|
|
LCache *cache; /* [n_layers] */
|
|
uint64_t clock, hits, miss;
|
|
float **K, **V; int kv_len, max_t;
|
|
double dense_load_s;
|
|
/* IMPROVEMENT 2: expert frequency heatmap */
|
|
uint32_t *freq;
|
|
int freq_token_count, hot_pinned, hot_n, warmup_tokens;
|
|
/* IMPROVEMENT 3: adaptive rebalance */
|
|
int token_count, rebal_interval, total_cap;
|
|
/* PREDICTION IMPROVEMENT A: per-layer smoothed gate logits across tokens.
|
|
* momentum_logits[l*E .. (l+1)*E-1] = EMA of recent gate outputs.
|
|
* Blended with fresh gate prediction: final = (1-smooth)*fresh + smooth*ema.
|
|
* Captures routing consistency across tokens (same token tends to reuse experts). */
|
|
float *momentum_logits; /* [n_layers * n_experts], EMA of gate logits */
|
|
float pilot_smooth; /* SMOOTH env: EMA coefficient 0.0-0.9 (default 0.3) */
|
|
uint8_t *is_pinned; /* [n_layers * n_experts], 1 if expert is globally pinned */
|
|
uint8_t *is_queued; /* [n_layers * n_experts], 1 if expert is currently in the prefetch queue */
|
|
float pilot_conf_limit; /* CONF_LIMIT env: cumulative gate probability threshold (e.g. 0.92) */
|
|
} Model;
|
|
|
|
static pthread_mutex_t g_pilot_mx = PTHREAD_MUTEX_INITIALIZER;
|
|
static struct { int l, e; } pilot_q[4096];
|
|
static volatile unsigned pilot_r = 0, pilot_w = 0;
|
|
static Model *pilot_m = NULL;
|
|
static int g_pilot = 0;
|
|
static int g_wide = 1; /* IMPROVEMENT 4: top-K * g_wide candidates prefetched */
|
|
|
|
static void pilot_prefetch(Model *m, int lnext, const float *x, int S);
|
|
static void *pilot_worker(void *arg);
|
|
static void ensure_pilot_worker_started(Model *m);
|
|
static void slot_ensure_allocated(Model *m, Slot *s);
|
|
|
|
static void ensure_pilot_worker_started(Model *m) {
|
|
if (!pilot_m) {
|
|
pilot_m = m;
|
|
pthread_t t;
|
|
pthread_create(&t, NULL, pilot_worker, NULL);
|
|
}
|
|
}
|
|
|
|
/* ---------- utility ---------- */
|
|
static double now_s(void) { struct timespec t; clock_gettime(CLOCK_MONOTONIC, &t); return t.tv_sec + t.tv_nsec*1e-9; }
|
|
#if defined(__APPLE__)
|
|
static double rss_gb(void) { struct rusage r; getrusage(RUSAGE_SELF, &r); return r.ru_maxrss / (1024.0*1024.0*1024.0); } /* macOS: byte */
|
|
#else
|
|
static double rss_gb(void) { struct rusage r; getrusage(RUSAGE_SELF, &r); return r.ru_maxrss / (1024.0*1024.0); } /* Linux: KB */
|
|
#endif
|
|
static float *falloc(int64_t n) { float *p = malloc(n*sizeof(float)); if(!p){fprintf(stderr,"OOM %ld\n",(long)n);exit(1);} return p; }
|
|
|
|
/* y[S,O] = x[S,I] @ W^T, W e' [O,I] row-major */
|
|
static void matmul(float *y, const float *x, const float *W, int S, int I, int O) {
|
|
#pragma omp parallel for schedule(static)
|
|
for (int o = 0; o < O; o++) {
|
|
const float *w = W + (int64_t)o * I;
|
|
for (int s = 0; s < S; s++) {
|
|
const float *xs = x + (int64_t)s * I;
|
|
float acc = 0.f;
|
|
for (int i = 0; i < I; i++) acc += xs[i] * w[i];
|
|
y[(int64_t)s * O + o] = acc;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* y[1,O] = x[1,I] @ W^T con W quantizzato: q[O,I] int8 + scala per riga.
|
|
* W[o,i] ~= q[o,i]*scale[o] -> y[o] = scale[o] * sum_i x[i]*q[o,i].
|
|
* Su ARM: attivazione quantizzata Q8_0 (scala per blocco di 16) + dot int8
|
|
* NEON (sdot dove c'e' dotprod) — stessa famiglia IDOT di glm.c, IDOT=0 per
|
|
* la via scalare byte-esatta. Misurato 2.7x end-to-end su M5. */
|
|
#if defined(__ARM_NEON)
|
|
#include <arm_neon.h>
|
|
static inline int32_t dot_i8_16(const int8_t *a, const int8_t *b) {
|
|
int32x4_t acc = vdupq_n_s32(0);
|
|
int8x16_t va = vld1q_s8(a), vb = vld1q_s8(b);
|
|
#if defined(__ARM_FEATURE_DOTPROD)
|
|
acc = vdotq_s32(acc, va, vb);
|
|
#else
|
|
acc = vpadalq_s16(acc, vmull_s8(vget_low_s8(va), vget_low_s8(vb)));
|
|
acc = vpadalq_s16(acc, vmull_s8(vget_high_s8(va), vget_high_s8(vb)));
|
|
#endif
|
|
return vaddvq_s32(acc);
|
|
}
|
|
#endif
|
|
static void matmul_q(float *y, const float *x, const int8_t *q, const float *scale, int I, int O) {
|
|
#if defined(__ARM_NEON)
|
|
static int idot = -1;
|
|
if (idot < 0) { const char *e = getenv("IDOT"); idot = !(e && *e == '0'); }
|
|
if (idot && I % 16 == 0 && I <= 4096) {
|
|
int nb = I / 16; int8_t xi[4096]; float xs[256];
|
|
for (int b = 0; b < nb; b++) {
|
|
const float *xb = x + b*16;
|
|
float am = 0.f; for (int i = 0; i < 16; i++) { float a = fabsf(xb[i]); if (a > am) am = a; }
|
|
float s = am/127.f; if (s < 1e-12f) s = 1e-12f;
|
|
xs[b] = s; float inv = 1.f/s;
|
|
for (int i = 0; i < 16; i++) xi[b*16+i] = (int8_t)lrintf(xb[i]*inv);
|
|
}
|
|
#pragma omp parallel for schedule(static)
|
|
for (int o = 0; o < O; o++) {
|
|
const int8_t *w = q + (int64_t)o * I;
|
|
float acc = 0.f;
|
|
for (int b = 0; b < nb; b++) acc += xs[b]*(float)dot_i8_16(xi+b*16, w+b*16);
|
|
y[o] = acc * scale[o];
|
|
}
|
|
return;
|
|
}
|
|
#endif
|
|
#pragma omp parallel for schedule(static)
|
|
for (int o = 0; o < O; o++) {
|
|
const int8_t *w = q + (int64_t)o * I;
|
|
float acc = 0.f;
|
|
for (int i = 0; i < I; i++) acc += x[i] * (float)w[i];
|
|
y[o] = acc * scale[o];
|
|
}
|
|
}
|
|
|
|
/* quantizza un weight f32 [O,I] -> int8 q[O,I] + scala[O], simmetrica per riga.
|
|
* Replica quant_dequant() del Python: scale = amax(|w|, riga)/qmax, q = round(w/scale). */
|
|
static void quantize_rows(const float *w, int8_t *q, float *scale, int O, int I, int bits) {
|
|
int qmax = (1 << (bits - 1)) - 1; /* 8->127, 4->7, 2->1 */
|
|
#pragma omp parallel for schedule(static)
|
|
for (int o = 0; o < O; o++) {
|
|
const float *wr = w + (int64_t)o * I;
|
|
float amax = 0.f; for (int i = 0; i < I; i++) { float a = fabsf(wr[i]); if (a > amax) amax = a; }
|
|
float s = amax / qmax; if (s < 1e-8f) s = 1e-8f;
|
|
scale[o] = s;
|
|
int8_t *qr = q + (int64_t)o * I;
|
|
for (int i = 0; i < I; i++) {
|
|
int v = (int)lrintf(wr[i] / s);
|
|
if (v > qmax) v = qmax;
|
|
if (v < -qmax-1) v = -qmax-1;
|
|
qr[i] = (int8_t)v;
|
|
}
|
|
}
|
|
}
|
|
|
|
/* rmsnorm su una riga di lunghezza D, in-place su out (out puo' essere == x) */
|
|
static void rmsnorm_row(float *out, const float *x, const float *w, int D, float eps) {
|
|
double ms = 0; for (int i = 0; i < D; i++) ms += (double)x[i]*x[i];
|
|
float r = 1.f / sqrtf((float)(ms / D) + eps);
|
|
for (int i = 0; i < D; i++) out[i] = x[i] * r * w[i];
|
|
}
|
|
|
|
static void softmax_row(float *x, int n) {
|
|
float m = -1e30f; for (int i = 0; i < n; i++) if (x[i] > m) m = x[i];
|
|
float s = 0; for (int i = 0; i < n; i++) { x[i] = expf(x[i]-m); s += x[i]; }
|
|
for (int i = 0; i < n; i++) x[i] /= s;
|
|
}
|
|
|
|
/* ---------- caricamento ---------- */
|
|
static void load_cfg(Cfg *c, const char *snap) {
|
|
char path[2048]; snprintf(path, sizeof(path), "%s/config.json", snap);
|
|
FILE *f = fopen(path, "rb"); if(!f){perror(path);exit(1);}
|
|
fseek(f,0,SEEK_END); long n=ftell(f); fseek(f,0,SEEK_SET);
|
|
char *buf = malloc(n+1); if(fread(buf,1,n,f)!=(size_t)n){} buf[n]=0; fclose(f);
|
|
char *arena=NULL; jval *r = json_parse(buf, &arena);
|
|
c->hidden = (int)json_get(r,"hidden_size")->num;
|
|
c->n_layers = (int)json_get(r,"num_hidden_layers")->num;
|
|
c->n_heads = (int)json_get(r,"num_attention_heads")->num;
|
|
c->n_kv_heads= (int)json_get(r,"num_key_value_heads")->num;
|
|
c->n_experts = (int)json_get(r,"num_experts")->num;
|
|
c->topk = (int)json_get(r,"num_experts_per_tok")->num;
|
|
c->inter = (int)json_get(r,"intermediate_size")->num;
|
|
c->vocab = (int)json_get(r,"vocab_size")->num;
|
|
c->head_dim = c->hidden / c->n_heads;
|
|
jval *th = json_get(r,"rope_theta"); c->theta = th ? (float)th->num : 10000.f;
|
|
jval *ep = json_get(r,"rms_norm_eps"); c->eps = ep ? (float)ep->num : 1e-5f;
|
|
jval *nt = json_get(r,"norm_topk_prob"); c->norm_topk = (nt && nt->t==J_BOOL) ? nt->boolean : 0;
|
|
free(buf); free(arena);
|
|
}
|
|
|
|
static float *load_t(Model *m, const char *name) {
|
|
int64_t n = st_numel(&m->S, name);
|
|
if (n < 0) { fprintf(stderr, "missing %s\n", name); exit(1); }
|
|
float *p = falloc(n);
|
|
st_read_f32(&m->S, name, p, 0); /* densa: niente DONTNEED, resta residente */
|
|
return p;
|
|
}
|
|
|
|
static void model_init(Model *m, const char *snap, int cap, int bits) {
|
|
memset(m, 0, sizeof(*m));
|
|
m->quant_bits = bits;
|
|
load_cfg(&m->c, snap);
|
|
st_init(&m->S, snap);
|
|
Cfg *c = &m->c;
|
|
double t0 = now_s();
|
|
m->embed = load_t(m, "model.embed_tokens.weight");
|
|
m->lm_head = load_t(m, "lm_head.weight");
|
|
m->final_norm = load_t(m, "model.norm.weight");
|
|
m->L = calloc(c->n_layers, sizeof(Layer));
|
|
char nm[256];
|
|
for (int i = 0; i < c->n_layers; i++) {
|
|
Layer *l = &m->L[i];
|
|
#define LD(field, suffix) snprintf(nm,sizeof(nm),"model.layers.%d." suffix,i); l->field = load_t(m,nm)
|
|
LD(in_ln, "input_layernorm.weight");
|
|
LD(post_ln,"post_attention_layernorm.weight");
|
|
LD(q, "self_attn.q_proj.weight"); LD(k, "self_attn.k_proj.weight");
|
|
LD(v, "self_attn.v_proj.weight"); LD(o, "self_attn.o_proj.weight");
|
|
LD(qn,"self_attn.q_norm.weight"); LD(kn,"self_attn.k_norm.weight");
|
|
LD(gate, "mlp.gate.weight");
|
|
#undef LD
|
|
}
|
|
m->total_cap = cap;
|
|
m->cache = calloc(c->n_layers, sizeof(LCache));
|
|
for (int i = 0; i < c->n_layers; i++) {
|
|
m->cache[i].cap = cap;
|
|
m->cache[i].slots = calloc(cap, sizeof(Slot));
|
|
}
|
|
/* IMPROVEMENT 2: frequency heatmap for hot expert pinning */
|
|
m->freq = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint32_t));
|
|
m->hot_pinned = 0; m->freq_token_count = 0;
|
|
m->hot_n = getenv("HOT") ? atoi(getenv("HOT")) : 0;
|
|
m->warmup_tokens = getenv("WARMUP") ? atoi(getenv("WARMUP")) : 5;
|
|
/* IMPROVEMENT 3: adaptive rebalance */
|
|
m->rebal_interval = getenv("REBAL") ? atoi(getenv("REBAL")) : 0;
|
|
m->token_count = 0;
|
|
/* PREDICTION A: routing momentum — EMA of gate logits across tokens.
|
|
* Initialized to zero; first token sets EMA = fresh logits. */
|
|
m->momentum_logits = calloc((size_t)c->n_layers * c->n_experts, sizeof(float));
|
|
float sv = getenv("SMOOTH") ? (float)atof(getenv("SMOOTH")) : 0.3f;
|
|
if (sv < 0.f) sv = 0.f; if (sv > 0.95f) sv = 0.95f;
|
|
m->pilot_smooth = sv;
|
|
m->is_pinned = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint8_t));
|
|
m->is_queued = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint8_t));
|
|
float cl = getenv("CONF_LIMIT") ? (float)atof(getenv("CONF_LIMIT")) : 0.92f;
|
|
if (cl < 0.1f) cl = 0.1f; if (cl > 1.0f) cl = 1.0f;
|
|
m->pilot_conf_limit = cl;
|
|
m->dense_load_s = now_s() - t0;
|
|
|
|
// Persistent Hot Pinning: try to load hot_pinned.bin
|
|
char pinpath[512];
|
|
snprintf(pinpath, sizeof(pinpath), "%s/hot_pinned.bin", snap);
|
|
FILE *pinf = fopen(pinpath, "rb");
|
|
if (pinf) {
|
|
size_t expected_size = (size_t)c->n_layers * c->n_experts;
|
|
if (fread(m->is_pinned, 1, expected_size, pinf) == expected_size) {
|
|
m->hot_pinned = 1;
|
|
printf("[HOT] Loaded persistent pinning from %s\n", pinpath);
|
|
|
|
if (g_pilot) {
|
|
ensure_pilot_worker_started(m);
|
|
for (int l = 0; l < c->n_layers; l++) {
|
|
for (int e = 0; e < c->n_experts; e++) {
|
|
if (m->is_pinned[l * c->n_experts + e]) {
|
|
unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
|
|
unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
if (w - r < 4096) {
|
|
pilot_q[w & 4095].l = l; pilot_q[w & 4095].e = e;
|
|
m->is_queued[l * c->n_experts + e] = 1;
|
|
__atomic_store_n(&pilot_w, w + 1, __ATOMIC_RELEASE);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
printf("[HOT] Pre-loading pinned experts into cache...\n");
|
|
double t_wait = now_s();
|
|
while (1) {
|
|
unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_ACQUIRE);
|
|
if (r == w) break;
|
|
sleep_ms(2);
|
|
}
|
|
printf("[HOT] Pre-loaded in %.1fs!\n", now_s() - t_wait);
|
|
}
|
|
}
|
|
fclose(pinf);
|
|
}
|
|
}
|
|
|
|
|
|
static void slot_ensure_allocated(Model *m, Slot *s) {
|
|
if (s->g) return;
|
|
Cfg *c = &m->c;
|
|
int64_t ng = (int64_t)c->inter * c->hidden;
|
|
int64_t nd = (int64_t)c->hidden * c->inter;
|
|
int8_t *w_block = malloc(ng + ng + nd);
|
|
s->g = w_block;
|
|
s->u = w_block + ng;
|
|
s->d = w_block + ng + ng;
|
|
float *s_block = falloc(c->inter + c->inter + c->hidden);
|
|
s->gs = s_block;
|
|
s->us = s_block + c->inter;
|
|
s->ds = s_block + c->inter + c->inter;
|
|
s->pinned = 0;
|
|
}
|
|
|
|
static void load_expert_merged(Model *m, int layer, int eid, Slot *s) {
|
|
char nm[256], qsnm[256];
|
|
snprintf(nm, sizeof(nm), "model.layers.%d.mlp.experts.%d.merged_weight", layer, eid);
|
|
snprintf(qsnm, sizeof(qsnm), "model.layers.%d.mlp.experts.%d.qs", layer, eid);
|
|
st_read_raw(&m->S, nm, s->g, 1);
|
|
st_read_raw(&m->S, qsnm, s->gs, 1);
|
|
}
|
|
|
|
/* ---------- cache expert: ritorna i pesi quantizzati (q+scale) da cache o disco ---------- */
|
|
static void expert_get(Model *m, int layer, int eid, Slot **out) {
|
|
LCache *lc = &m->cache[layer];
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
for (int i = 0; i < lc->n; i++) if (lc->slots[i].eid == eid) {
|
|
m->hits++; lc->layer_hits++; lc->slots[i].used = ++m->clock; *out = &lc->slots[i];
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
return;
|
|
}
|
|
m->miss++; lc->layer_miss++;
|
|
Cfg *c = &m->c;
|
|
Slot *s;
|
|
if (lc->n < lc->cap) {
|
|
s = &lc->slots[lc->n++];
|
|
slot_ensure_allocated(m, s);
|
|
} else {
|
|
/* IMPROVEMENT 2: LRU eviction — never evict pinned experts */
|
|
int lru = -1;
|
|
for (int i = 0; i < lc->n; i++) {
|
|
if (lc->slots[i].pinned) continue;
|
|
if (lru < 0 || lc->slots[i].used < lc->slots[lru].used) lru = i;
|
|
}
|
|
if (lru < 0) lru = 0; /* all pinned: fallback evict oldest */
|
|
s = &lc->slots[lru];
|
|
s->pinned = 0;
|
|
}
|
|
s->eid = -1;
|
|
s->used = ++m->clock;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
|
|
load_expert_merged(m, layer, eid, s);
|
|
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
s->eid = eid;
|
|
s->pinned = m->is_pinned[layer * c->n_experts + eid];
|
|
s->used = ++m->clock;
|
|
*out = s;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
}
|
|
|
|
/* ---------- IMPROVEMENT 2: pin top-N hot experts per layer ---------- */
|
|
static void pin_hot_experts(Model *m) {
|
|
Cfg *c = &m->c;
|
|
if (m->hot_n <= 0 || m->hot_pinned) return;
|
|
m->hot_pinned = 1;
|
|
|
|
int is_dynamic = (m->hot_n >= 100);
|
|
double thresh = is_dynamic ? (double)m->hot_n / 1000.0 : 0.0;
|
|
|
|
int pinned_total = 0;
|
|
for (int l = 0; l < c->n_layers; l++) {
|
|
uint32_t *freq_l = m->freq + (int64_t)l * c->n_experts;
|
|
|
|
uint64_t layer_total = 0;
|
|
for (int e = 0; e < c->n_experts; e++) layer_total += freq_l[e];
|
|
if (layer_total == 0) continue;
|
|
|
|
int max_pin = m->cache[l].cap - 8;
|
|
if (max_pin < 4) max_pin = 4;
|
|
|
|
int hn = is_dynamic ? max_pin : (m->hot_n < c->n_experts ? m->hot_n : c->n_experts);
|
|
int hot_eids[256];
|
|
int actual_hn = 0;
|
|
|
|
for (int k = 0; k < hn; k++) {
|
|
int best = -1; uint32_t bv = 0;
|
|
for (int e = 0; e < c->n_experts; e++) {
|
|
int already = 0;
|
|
for (int j = 0; j < k; j++) if (hot_eids[j] == e) { already = 1; break; }
|
|
if (!already && freq_l[e] > bv) { bv = freq_l[e]; best = e; }
|
|
}
|
|
if (best < 0 || bv == 0) break;
|
|
if (is_dynamic && bv < thresh * layer_total) break;
|
|
hot_eids[k] = best;
|
|
actual_hn++;
|
|
}
|
|
|
|
for (int k = 0; k < actual_hn; k++) {
|
|
int eid = hot_eids[k];
|
|
m->is_pinned[l * c->n_experts + eid] = 1;
|
|
|
|
LCache *lc = &m->cache[l];
|
|
int found = 0;
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
for (int i = 0; i < lc->n; i++) {
|
|
if (lc->slots[i].eid == eid) { lc->slots[i].pinned = 1; found = 1; break; }
|
|
}
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
if (!found) {
|
|
unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
|
|
unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
if (w - r < 4096) {
|
|
pilot_q[w & 4095].l = l; pilot_q[w & 4095].e = eid;
|
|
__atomic_store_n(&pilot_w, w + 1, __ATOMIC_RELEASE);
|
|
}
|
|
}
|
|
pinned_total++;
|
|
}
|
|
}
|
|
if (is_dynamic) {
|
|
printf("[HOT] Dynamic Pinned %d experts total (thresh=%.1f%%) after %d warmup tokens\n",
|
|
pinned_total, thresh * 100.0, m->freq_token_count);
|
|
} else {
|
|
printf("[HOT] Pinned %d experts (top-%d/layer) after %d warmup tokens\n",
|
|
pinned_total, m->hot_n, m->freq_token_count);
|
|
}
|
|
}
|
|
|
|
/* ---------- IMPROVEMENT 3: adaptive per-layer cache rebalancing ---------- */
|
|
static void rebalance_cache(Model *m) {
|
|
Cfg *c = &m->c;
|
|
uint64_t total_miss = 0;
|
|
for (int l = 0; l < c->n_layers; l++) total_miss += m->cache[l].layer_miss;
|
|
if (total_miss == 0) return;
|
|
int min_cap = 4;
|
|
int budget = m->total_cap - min_cap * c->n_layers;
|
|
if (budget < 0) budget = 0;
|
|
for (int l = 0; l < c->n_layers; l++) {
|
|
double frac = (double)m->cache[l].layer_miss / (double)total_miss;
|
|
int new_cap = min_cap + (int)(frac * budget + 0.5);
|
|
LCache *lc = &m->cache[l];
|
|
if (new_cap > lc->cap) {
|
|
Slot *ns = realloc(lc->slots, new_cap * sizeof(Slot));
|
|
if (ns) {
|
|
memset(ns + lc->cap, 0, (new_cap - lc->cap) * sizeof(Slot));
|
|
lc->slots = ns; lc->cap = new_cap;
|
|
}
|
|
}
|
|
lc->layer_hits = 0; lc->layer_miss = 0;
|
|
}
|
|
}
|
|
|
|
/* ---------- RoPE su un vettore di una testa (head_dim) a posizione assoluta pos ---------- */
|
|
static void rope_head(float *x, int pos, const Cfg *c) {
|
|
int h = c->head_dim / 2;
|
|
for (int j = 0; j < h; j++) {
|
|
float inv = powf(c->theta, -2.0f * j / c->head_dim);
|
|
float ang = pos * inv, cs = cosf(ang), sn = sinf(ang);
|
|
float a = x[j], b = x[j+h];
|
|
x[j] = a*cs - b*sn;
|
|
x[j+h] = b*cs + a*sn;
|
|
}
|
|
}
|
|
|
|
/* attenzione sui token nuovi x[S,hidden]; pos_base = posizione assoluta del primo token nuovo */
|
|
static void attention(Model *m, Layer *l, int layer, float *x, int S, int pos_base, float *out) {
|
|
Cfg *c = &m->c; int H = c->n_heads, hd = c->head_dim, D = c->hidden;
|
|
float *q = falloc((int64_t)S*D), *k = falloc((int64_t)S*D), *vv = falloc((int64_t)S*D);
|
|
matmul(q, x, l->q, S, D, D);
|
|
matmul(k, x, l->k, S, D, D);
|
|
matmul(vv, x, l->v, S, D, D);
|
|
/* qk-norm sull'intero vettore hidden, poi RoPE per testa */
|
|
for (int s = 0; s < S; s++) {
|
|
rmsnorm_row(q + (int64_t)s*D, q + (int64_t)s*D, l->qn, D, c->eps);
|
|
rmsnorm_row(k + (int64_t)s*D, k + (int64_t)s*D, l->kn, D, c->eps);
|
|
int pos = pos_base + s;
|
|
for (int hh = 0; hh < H; hh++) { rope_head(q + (int64_t)s*D + hh*hd, pos, c); rope_head(k + (int64_t)s*D + hh*hd, pos, c); }
|
|
}
|
|
/* scrive k,v nella kv-cache alle posizioni pos_base..pos_base+S-1 */
|
|
for (int s = 0; s < S; s++) for (int hh = 0; hh < H; hh++) {
|
|
int t = pos_base + s;
|
|
memcpy(m->K[layer] + ((int64_t)hh*m->max_t + t)*hd, k + (int64_t)s*D + hh*hd, hd*sizeof(float));
|
|
memcpy(m->V[layer] + ((int64_t)hh*m->max_t + t)*hd, vv + (int64_t)s*D + hh*hd, hd*sizeof(float));
|
|
}
|
|
int Tk = pos_base + S; /* numero di key totali disponibili */
|
|
float scale = 1.f / sqrtf((float)hd);
|
|
float *ctx = falloc((int64_t)S*D);
|
|
#pragma omp parallel for collapse(2) schedule(static)
|
|
for (int hh = 0; hh < H; hh++) {
|
|
for (int s = 0; s < S; s++) {
|
|
int qpos = pos_base + s;
|
|
const float *qv = q + (int64_t)s*D + hh*hd;
|
|
float sc[4096];
|
|
for (int t = 0; t <= qpos; t++) { /* causale: t <= qpos */
|
|
const float *kv = m->K[layer] + ((int64_t)hh*m->max_t + t)*hd;
|
|
float acc = 0; for (int dd = 0; dd < hd; dd++) acc += qv[dd]*kv[dd];
|
|
sc[t] = acc * scale;
|
|
}
|
|
softmax_row(sc, qpos+1);
|
|
float *cx = ctx + (int64_t)s*D + hh*hd;
|
|
for (int dd = 0; dd < hd; dd++) cx[dd] = 0;
|
|
for (int t = 0; t <= qpos; t++) {
|
|
const float *vrow = m->V[layer] + ((int64_t)hh*m->max_t + t)*hd;
|
|
float a = sc[t];
|
|
for (int dd = 0; dd < hd; dd++) cx[dd] += a * vrow[dd];
|
|
}
|
|
}
|
|
}
|
|
(void)Tk;
|
|
matmul(out, ctx, l->o, S, D, D);
|
|
free(q); free(k); free(vv); free(ctx);
|
|
}
|
|
|
|
/* MoE sui token x[S,hidden] -> out[S,hidden] */
|
|
static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) {
|
|
Cfg *c = &m->c; int D = c->hidden, E = c->n_experts, K = c->topk, I = c->inter;
|
|
float *logits = falloc((int64_t)S*E);
|
|
matmul(logits, x, l->gate, S, D, E);
|
|
memset(out, 0, (int64_t)S*D*sizeof(float));
|
|
float *g = falloc(I), *u = falloc(I), *hh = falloc(D);
|
|
for (int s = 0; s < S; s++) {
|
|
float *pr = logits + (int64_t)s*E;
|
|
if (layer == 0 && m->momentum_logits) {
|
|
float *ema = m->momentum_logits;
|
|
int is_zero = 1;
|
|
for (int e = 0; e < E; e++) { if (ema[e] != 0.f) { is_zero = 0; break; } }
|
|
if (is_zero) {
|
|
for (int e = 0; e < E; e++) ema[e] = pr[e];
|
|
} else {
|
|
for (int e = 0; e < E; e++) {
|
|
ema[e] = (1.f - m->pilot_smooth) * pr[e] + m->pilot_smooth * ema[e];
|
|
}
|
|
}
|
|
}
|
|
|
|
softmax_row(pr, E);
|
|
/* top-K indici (selezione parziale) */
|
|
int idx[64]; float val[64];
|
|
for (int kk = 0; kk < K; kk++) {
|
|
int best = -1; float bv = -1e30f;
|
|
for (int e = 0; e < E; e++) {
|
|
int taken = 0; for (int j = 0; j < kk; j++) if (idx[j]==e){taken=1;break;}
|
|
if (!taken && pr[e] > bv) { bv = pr[e]; best = e; }
|
|
}
|
|
idx[kk] = best; val[kk] = bv;
|
|
}
|
|
if (c->norm_topk) { float sm=0; for(int kk=0;kk<K;kk++) sm+=val[kk]; for(int kk=0;kk<K;kk++) val[kk]/=sm; }
|
|
/* IMPROVEMENT 2: update activation heatmap (before pinning activates) */
|
|
if (!m->hot_pinned && m->freq) {
|
|
uint32_t *freq_l = m->freq + (int64_t)layer * E;
|
|
for (int kk = 0; kk < K; kk++) if (idx[kk] >= 0) freq_l[idx[kk]]++;
|
|
}
|
|
const float *xs = x + (int64_t)s*D;
|
|
for (int kk = 0; kk < K; kk++) {
|
|
Slot *e; expert_get(m, layer, idx[kk], &e);
|
|
matmul_q(g, xs, e->g, e->gs, D, I); /* gate_proj [I,D] */
|
|
matmul_q(u, xs, e->u, e->us, D, I); /* up_proj [I,D] */
|
|
for (int i = 0; i < I; i++) { float gv = g[i]; g[i] = (gv / (1.f + expf(-gv))) * u[i]; }
|
|
matmul_q(hh, g, e->d, e->ds, I, D); /* down_proj [D,I] */
|
|
float w = val[kk];
|
|
float *os = out + (int64_t)s*D;
|
|
for (int d = 0; d < D; d++) os[d] += w * hh[d];
|
|
}
|
|
}
|
|
free(logits); free(g); free(u); free(hh);
|
|
}
|
|
|
|
static float *step(Model *m, const int *ids, int S, int pos_base) {
|
|
Cfg *c = &m->c; int D = c->hidden;
|
|
if (g_pilot && m->token_count > 0) {
|
|
unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
__atomic_store_n(&pilot_w, r, __ATOMIC_RELEASE);
|
|
memset(m->is_queued, 0, (size_t)c->n_layers * c->n_experts);
|
|
}
|
|
float *x = falloc((int64_t)S*D);
|
|
for (int s = 0; s < S; s++) memcpy(x + (int64_t)s*D, m->embed + (int64_t)ids[s]*D, D*sizeof(float));
|
|
float *nrm = falloc((int64_t)S*D), *tmp = falloc((int64_t)S*D);
|
|
for (int i = 0; i < c->n_layers; i++) {
|
|
Layer *l = &m->L[i];
|
|
for (int s = 0; s < S; s++) rmsnorm_row(nrm + (int64_t)s*D, x + (int64_t)s*D, l->in_ln, D, c->eps);
|
|
attention(m, l, i, nrm, S, pos_base, tmp);
|
|
for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j];
|
|
/* IMPROVEMENT 1: PILOT=1 -> 1-layer lookahead */
|
|
if (g_pilot >= 1 && S <= 8 && i + 1 < c->n_layers)
|
|
pilot_prefetch(m, i + 1, x, S);
|
|
for (int s = 0; s < S; s++) rmsnorm_row(nrm + (int64_t)s*D, x + (int64_t)s*D, l->post_ln, D, c->eps);
|
|
moe(m, l, i, nrm, S, tmp);
|
|
for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j];
|
|
|
|
/* PREDICTION IMPROVEMENT C (Residual gate trick):
|
|
* PILOT=2 -> prefetch layer i+2 using completed state x (containing MoE residual). */
|
|
if (g_pilot >= 2 && S <= 8 && i + 2 < c->n_layers)
|
|
pilot_prefetch(m, i + 2, x, S);
|
|
if (g_pilot >= 3 && S <= 8 && i + 3 < c->n_layers)
|
|
pilot_prefetch(m, i + 3, x, S);
|
|
|
|
}
|
|
/* IMPROVEMENT 2: count tokens; trigger hot pinning after warmup */
|
|
m->token_count++; m->freq_token_count++;
|
|
if (!m->hot_pinned && m->hot_n > 0 && m->freq_token_count >= m->warmup_tokens)
|
|
pin_hot_experts(m);
|
|
/* IMPROVEMENT 3: periodic adaptive rebalance */
|
|
if (m->rebal_interval > 0 && m->token_count % m->rebal_interval == 0)
|
|
rebalance_cache(m);
|
|
m->kv_len = pos_base + S;
|
|
float *last = falloc(D);
|
|
rmsnorm_row(last, x + (int64_t)(S-1)*D, m->final_norm, D, c->eps);
|
|
float *logit = falloc(c->vocab);
|
|
matmul(logit, last, m->lm_head, 1, D, c->vocab);
|
|
free(x); free(nrm); free(tmp); free(last);
|
|
return logit;
|
|
}
|
|
|
|
static void pilot_realload(Model *m, int layer, int eid) {
|
|
LCache *lc = &m->cache[layer];
|
|
Cfg *c = &m->c;
|
|
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
for (int i = 0; i < lc->n; i++) {
|
|
if (lc->slots[i].eid == eid) {
|
|
m->is_queued[layer * c->n_experts + eid] = 0;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
return;
|
|
}
|
|
}
|
|
Slot *s;
|
|
if (lc->n < lc->cap) {
|
|
s = &lc->slots[lc->n++];
|
|
slot_ensure_allocated(m, s);
|
|
} else {
|
|
/* IMPROVEMENT 2: never evict pinned experts */
|
|
int lru = -1;
|
|
for (int i = 0; i < lc->n; i++) {
|
|
if (lc->slots[i].pinned) continue;
|
|
if (lru < 0 || lc->slots[i].used < lc->slots[lru].used) lru = i;
|
|
}
|
|
if (lru < 0) {
|
|
m->is_queued[layer * c->n_experts + eid] = 0;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
return; /* all pinned, skip */
|
|
}
|
|
s = &lc->slots[lru]; s->pinned = 0;
|
|
}
|
|
s->eid = -1; s->used = ++m->clock;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
|
|
load_expert_merged(m, layer, eid, s);
|
|
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
s->eid = eid;
|
|
s->pinned = m->is_pinned[layer * c->n_experts + eid];
|
|
s->used = ++m->clock;
|
|
m->is_queued[layer * c->n_experts + eid] = 0;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
}
|
|
|
|
static void *pilot_worker(void *arg) {
|
|
(void)arg;
|
|
while (1) {
|
|
unsigned r = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
unsigned w = __atomic_load_n(&pilot_w, __ATOMIC_ACQUIRE);
|
|
if (r == w) {
|
|
sleep_ms(1);
|
|
continue;
|
|
}
|
|
int layer = pilot_q[r & 4095].l;
|
|
int eid = pilot_q[r & 4095].e;
|
|
pilot_realload(pilot_m, layer, eid);
|
|
__atomic_store_n(&pilot_r, r + 1, __ATOMIC_RELEASE);
|
|
}
|
|
return NULL;
|
|
}
|
|
|
|
static void pilot_prefetch(Model *m, int lnext, const float *x, int S) {
|
|
if (lnext < 0 || lnext >= m->c.n_layers) return;
|
|
Cfg *c = &m->c; int D = c->hidden, E = c->n_experts;
|
|
/* IMPROVEMENT 4: wide prefetch — top K * g_wide candidates */
|
|
int cand = c->topk * g_wide;
|
|
if (cand > E) cand = E;
|
|
ensure_pilot_worker_started(m);
|
|
float *logits = falloc((int64_t)S * E);
|
|
Layer *l = &m->L[lnext];
|
|
|
|
// PREDICTION IMPROVEMENT B: Apply RMSNorm to x using destination layer's post_ln
|
|
// This scales inputs to the distribution expected by l->gate.
|
|
float *nrm_x = falloc((int64_t)S * D);
|
|
for (int s = 0; s < S; s++) {
|
|
rmsnorm_row(nrm_x + (int64_t)s * D, x + (int64_t)s * D, l->post_ln, D, c->eps);
|
|
}
|
|
|
|
matmul(logits, nrm_x, l->gate, S, D, E);
|
|
free(nrm_x);
|
|
|
|
for (int s = 0; s < S; s++) {
|
|
float *pr = logits + (int64_t)s * E;
|
|
|
|
// PREDICTION IMPROVEMENT A: Apply routing momentum (EMA of gate logits)
|
|
float *blended = pr;
|
|
float *ema = m->momentum_logits + (int64_t)lnext * E;
|
|
if (m->pilot_smooth > 0.f) {
|
|
blended = falloc(E);
|
|
int is_zero = 1;
|
|
for (int e = 0; e < E; e++) { if (ema[e] != 0.f) { is_zero = 0; break; } }
|
|
if (is_zero) {
|
|
for (int e = 0; e < E; e++) {
|
|
ema[e] = pr[e];
|
|
blended[e] = pr[e];
|
|
}
|
|
} else {
|
|
for (int e = 0; e < E; e++) {
|
|
blended[e] = (1.f - m->pilot_smooth) * pr[e] + m->pilot_smooth * ema[e];
|
|
ema[e] = blended[e]; // update EMA
|
|
}
|
|
}
|
|
}
|
|
|
|
int cand = 0;
|
|
int idx[128];
|
|
|
|
float *exps = falloc(E);
|
|
float sum_exps = 0.f;
|
|
for (int e = 0; e < E; e++) {
|
|
exps[e] = expf(blended[e]);
|
|
sum_exps += exps[e];
|
|
}
|
|
|
|
float cum_sum = 0.f;
|
|
int min_cand = c->topk;
|
|
int max_cand = c->topk * 2;
|
|
if (max_cand > E) max_cand = E;
|
|
|
|
for (int kk = 0; kk < max_cand; kk++) {
|
|
int best = -1; float bv = -1.f;
|
|
for (int e = 0; e < E; e++) {
|
|
int taken = 0; for (int j = 0; j < kk; j++) if (idx[j] == e) { taken=1; break; }
|
|
if (!taken && exps[e] > bv) { bv = exps[e]; best = e; }
|
|
}
|
|
if (best < 0) break;
|
|
idx[kk] = best;
|
|
cum_sum += bv;
|
|
cand++;
|
|
if (cum_sum >= m->pilot_conf_limit * sum_exps && cand >= min_cand) {
|
|
break;
|
|
}
|
|
}
|
|
free(exps);
|
|
|
|
if (blended != pr) free(blended);
|
|
|
|
/* IMPROVEMENT 5: sort candidates by eid for sequential SSD read locality */
|
|
for (int a = 0; a < cand-1; a++)
|
|
for (int b = a+1; b < cand; b++)
|
|
if (idx[b] >= 0 && (idx[a] < 0 || idx[a] > idx[b])) { int t = idx[a]; idx[a] = idx[b]; idx[b] = t; }
|
|
|
|
for (int kk = 0; kk < cand; kk++) {
|
|
int eid = idx[kk];
|
|
if (eid < 0) continue;
|
|
int found = 0;
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
LCache *lc = &m->cache[lnext];
|
|
for (int z = 0; z < lc->n; z++) {
|
|
if (lc->slots[z].eid == eid) { found = 1; break; }
|
|
}
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
if (!found) {
|
|
int gidx = lnext * E + eid;
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
int already_queued = m->is_queued[gidx];
|
|
if (!already_queued) {
|
|
m->is_queued[gidx] = 1;
|
|
}
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
|
|
if (!already_queued) {
|
|
unsigned w2 = __atomic_load_n(&pilot_w, __ATOMIC_RELAXED);
|
|
unsigned r2 = __atomic_load_n(&pilot_r, __ATOMIC_ACQUIRE);
|
|
if (w2 - r2 < 4096) {
|
|
pilot_q[w2 & 4095].l = lnext;
|
|
pilot_q[w2 & 4095].e = eid;
|
|
__atomic_store_n(&pilot_w, w2 + 1, __ATOMIC_RELEASE);
|
|
} else {
|
|
pthread_mutex_lock(&g_pilot_mx);
|
|
m->is_queued[gidx] = 0;
|
|
pthread_mutex_unlock(&g_pilot_mx);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
free(logits);
|
|
}
|
|
|
|
|
|
/* generazione greedy. prompt[np] -> riempie out[np+n_new] */
|
|
static void generate(Model *m, const int *prompt, int np, int n_new, int *out) {
|
|
Cfg *c = &m->c;
|
|
m->max_t = np + n_new;
|
|
m->K = calloc(c->n_layers, sizeof(float*)); m->V = calloc(c->n_layers, sizeof(float*));
|
|
for (int i = 0; i < c->n_layers; i++) {
|
|
m->K[i] = falloc((int64_t)c->n_heads * m->max_t * c->head_dim);
|
|
m->V[i] = falloc((int64_t)c->n_heads * m->max_t * c->head_dim);
|
|
}
|
|
for (int i = 0; i < np; i++) out[i] = prompt[i];
|
|
float *logit = step(m, prompt, np, 0); /* PREFILL */
|
|
int len = np;
|
|
for (int s = 0; s < n_new; s++) {
|
|
int best = 0; float bv = logit[0];
|
|
for (int i = 1; i < c->vocab; i++) if (logit[i] > bv) { bv = logit[i]; best = i; }
|
|
free(logit);
|
|
out[len++] = best;
|
|
if (s == n_new - 1) break;
|
|
int one = best;
|
|
logit = step(m, &one, 1, len - 1); /* DECODE */
|
|
}
|
|
}
|
|
|
|
/* ---------- lettura ref.json ---------- */
|
|
static int *read_int_array(jval *o, const char *key, int *n_out) {
|
|
jval *a = json_get(o, key);
|
|
int *r = malloc(a->len * sizeof(int));
|
|
for (int i = 0; i < a->len; i++) r[i] = (int)a->kids[i]->num;
|
|
*n_out = a->len; return r;
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
const char *snap = getenv("SNAP");
|
|
if (!snap) { fprintf(stderr, "set SNAP=<snapshot directory>\n"); return 1; }
|
|
g_pilot = getenv("PILOT") ? atoi(getenv("PILOT")) : 0;
|
|
g_wide = getenv("WIDE") ? atoi(getenv("WIDE")) : 1;
|
|
if (g_wide < 1) g_wide = 1;
|
|
if (g_wide > 4) g_wide = 4;
|
|
int hot_n = getenv("HOT") ? atoi(getenv("HOT")) : 0;
|
|
int rebal = getenv("REBAL") ? atoi(getenv("REBAL")) : 0;
|
|
int cap = argc > 1 ? atoi(argv[1]) : 16;
|
|
int bits = argc > 2 ? atoi(argv[2]) : 8;
|
|
if (bits < 2 || bits > 8) {
|
|
fprintf(stderr, "quant_bits must be 2..8 (got %d)\n", bits);
|
|
return 1;
|
|
}
|
|
const char *refpath = argc > 3 ? argv[3] : "ref.json";
|
|
|
|
float smooth = getenv("SMOOTH") ? (float)atof(getenv("SMOOTH")) : 0.3f;
|
|
float conf = getenv("CONF_LIMIT") ? (float)atof(getenv("CONF_LIMIT")) : 0.92f;
|
|
|
|
printf("== Streaming C engine v2.2 | cache=%d/layer bits=%d pilot=%d wide=%d hot=%d rebal=%d smooth=%.2f conf=%.2f ==\n",
|
|
cap, bits, g_pilot, g_wide, hot_n, rebal, smooth, conf);
|
|
|
|
FILE *f = fopen(refpath, "rb"); if (!f) { perror(refpath); return 1; }
|
|
fseek(f,0,SEEK_END); long n=ftell(f); fseek(f,0,SEEK_SET);
|
|
char *buf=malloc(n+1); if (fread(buf,1,n,f)!=(size_t)n) {} buf[n]=0; fclose(f);
|
|
char *arena=NULL; jval *ref = json_parse(buf, &arena);
|
|
int np, nfull; int *prompt = read_int_array(ref,"prompt_ids",&np); int *full = read_int_array(ref,"full_ids",&nfull);
|
|
int n_new = nfull - np;
|
|
|
|
Model m; model_init(&m, snap, cap, bits);
|
|
printf("resident weights loaded in %.1fs | RSS after load: %.2f GB\n", m.dense_load_s, rss_gb());
|
|
|
|
int *out = malloc((np + n_new) * sizeof(int));
|
|
double t = now_s();
|
|
generate(&m, prompt, np, n_new, out);
|
|
double dt = now_s() - t;
|
|
|
|
int match = 0;
|
|
printf("\nReference: "); for (int i=np;i<nfull;i++) printf("%d ", full[i]);
|
|
printf("\nC engine : "); for (int i=np;i<nfull;i++) { printf("%d ", out[i]); if (out[i]==full[i]) match++; }
|
|
printf("\nMatching tokens: %d/%d\n", match, n_new);
|
|
double tot = m.hits + m.miss;
|
|
printf("\nPEAK RSS: %.2f GB\n", rss_gb());
|
|
printf("Expert cache hit rate: %.1f%% (hit=%llu miss=%llu)\n", tot?100.0*m.hits/tot:0.0,
|
|
(unsigned long long)m.hits, (unsigned long long)m.miss);
|
|
|
|
|
|
// Persistent Hot Pinning: save dynamic pinning if newly created
|
|
if (m.hot_pinned) {
|
|
char pinpath[512];
|
|
snprintf(pinpath, sizeof(pinpath), "%s/hot_pinned.bin", snap);
|
|
FILE *pinf_chk = fopen(pinpath, "rb");
|
|
if (!pinf_chk) {
|
|
FILE *pinf_save = fopen(pinpath, "wb");
|
|
if (pinf_save) {
|
|
size_t expected_size = (size_t)m.c.n_layers * m.c.n_experts;
|
|
fwrite(m.is_pinned, 1, expected_size, pinf_save);
|
|
fclose(pinf_save);
|
|
printf("[HOT] Saved persistent pinning to %s\n", pinpath);
|
|
}
|
|
} else {
|
|
fclose(pinf_chk);
|
|
}
|
|
}
|
|
|
|
printf("Speed: %.2f tok/s (%.1fs for %d tokens)\n", n_new/dt, dt, n_new);
|
|
free(buf); free(arena);
|
|
return 0;
|
|
}
|