diff --git a/c/olmoe.c b/c/olmoe.c index 5923bde..b0f9e8c 100644 --- a/c/olmoe.c +++ b/c/olmoe.c @@ -5,6 +5,14 @@ * 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 @@ -12,11 +20,22 @@ #include #include #include +#include #if defined(__APPLE__) || defined(__linux__) || defined(__FreeBSD__) #include +#include #endif #include "st.h" +#ifdef _WIN32 +#include +#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; @@ -33,22 +52,44 @@ typedef struct { * 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. */ -typedef struct { int eid; int8_t *g, *u, *d; float *gs, *us, *ds; uint64_t used; } Slot; -typedef struct { Slot *slots; int n, cap; } LCache; +/* 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; /* bit di quantizzazione degli expert (2..8); storage int8, niente f32 (#134) */ + int quant_bits; float *embed, *lm_head, *final_norm; Layer *L; LCache *cache; /* [n_layers] */ uint64_t clock, hits, miss; - /* kv-cache per-layer: K,V come [H * maxT * head_dim] */ 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 */ } 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); + /* ---------- 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__) @@ -209,8 +250,24 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { 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->dense_load_s = now_s() - t0; } @@ -233,10 +290,13 @@ static void load_expert_w(Model *m, const char *name, int8_t *q, float *scale, i /* ---------- 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->slots[i].used = ++m->clock; *out = &lc->slots[i]; return; + m->hits++; lc->layer_hits++; lc->slots[i].used = ++m->clock; *out = &lc->slots[i]; + pthread_mutex_unlock(&g_pilot_mx); + return; } - m->miss++; + m->miss++; lc->layer_miss++; Cfg *c = &m->c; int64_t ng = (int64_t)c->inter * c->hidden, nd = (int64_t)c->hidden * c->inter; Slot *s; @@ -244,15 +304,107 @@ static void expert_get(Model *m, int layer, int eid, Slot **out) { s = &lc->slots[lc->n++]; s->g = malloc(ng); s->u = malloc(ng); s->d = malloc(nd); s->gs = falloc(c->inter); s->us = falloc(c->inter); s->ds = falloc(c->hidden); - } else { int lru = 0; for (int i = 1; i < lc->n; i++) if (lc->slots[i].used < lc->slots[lru].used) lru = i; s = &lc->slots[lru]; } + s->pinned = 0; + } 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); + float *tmp = falloc(ng > nd ? ng : nd); char nm[256]; snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.gate_proj.weight",layer,eid); load_expert_w(m,nm,s->g,s->gs,c->inter,c->hidden,tmp); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.up_proj.weight", layer,eid); load_expert_w(m,nm,s->u,s->us,c->inter,c->hidden,tmp); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.%d.down_proj.weight",layer,eid); load_expert_w(m,nm,s->d,s->ds,c->hidden,c->inter,tmp); free(tmp); - s->eid = eid; s->used = ++m->clock; + + 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 hn = m->hot_n < c->n_experts ? m->hot_n : c->n_experts; + 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; + int hot_eids[256]; + /* Find top hn experts by activation frequency */ + 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) { hn = k; break; } + hot_eids[k] = best; + } + /* Mark already-cached hot experts as pinned; enqueue uncached ones */ + for (int k = 0; k < hn; k++) { + int eid = hot_eids[k]; + m->is_pinned[l * c->n_experts + eid] = 1; // Mark globally + + 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++; + } + } + 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 ---------- */ @@ -337,6 +489,11 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { idx[kk] = best; val[kk] = bv; } if (c->norm_topk) { float sm=0; for(int kk=0;kkhot_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); @@ -363,12 +520,26 @@ static float *step(Model *m, const int *ids, int S, int pos_base) { 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); } + /* 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; - /* solo l'ultimo token -> logits */ float *last = falloc(D); rmsnorm_row(last, x + (int64_t)(S-1)*D, m->final_norm, D, c->eps); float *logit = falloc(c->vocab); @@ -377,6 +548,155 @@ static float *step(Model *m, const int *ids, int S, int pos_base) { return logit; } +static void pilot_realload(Model *m, int layer, int eid) { + LCache *lc = &m->cache[layer]; + Cfg *c = &m->c; + int64_t ng = (int64_t)c->inter * c->hidden, nd = (int64_t)c->hidden * c->inter; + + pthread_mutex_lock(&g_pilot_mx); + for (int i = 0; i < lc->n; i++) { + if (lc->slots[i].eid == eid) { pthread_mutex_unlock(&g_pilot_mx); return; } + } + Slot *s; + if (lc->n < lc->cap) { + s = &lc->slots[lc->n++]; + s->g = malloc(ng); s->u = malloc(ng); s->d = malloc(nd); + s->gs = falloc(c->inter); s->us = falloc(c->inter); s->ds = falloc(c->hidden); + s->pinned = 0; + } 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) { 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); + + float *tmp = falloc(ng > nd ? ng : nd); + char nm[256]; + snprintf(nm, sizeof(nm), "model.layers.%d.mlp.experts.%d.gate_proj.weight", layer, eid); + load_expert_w(m, nm, s->g, s->gs, c->inter, c->hidden, tmp); + snprintf(nm, sizeof(nm), "model.layers.%d.mlp.experts.%d.up_proj.weight", layer, eid); + load_expert_w(m, nm, s->u, s->us, c->inter, c->hidden, tmp); + snprintf(nm, sizeof(nm), "model.layers.%d.mlp.experts.%d.down_proj.weight", layer, eid); + load_expert_w(m, nm, s->d, s->ds, c->hidden, c->inter, tmp); + free(tmp); + + pthread_mutex_lock(&g_pilot_mx); + s->eid = eid; + s->pinned = m->is_pinned[layer * c->n_experts + eid]; + s->used = ++m->clock; + 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; + if (!pilot_m) { + pilot_m = m; + pthread_t t; + pthread_create(&t, NULL, pilot_worker, NULL); + } + 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 idx[128]; /* up to topk * 4 */ + for (int kk = 0; kk < cand; 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 && blended[e] > bv) { bv = blended[e]; best = e; } + } + idx[kk] = best; + } + + 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) { + 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); + } + } + } + } + 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; @@ -411,22 +731,30 @@ static int *read_int_array(jval *o, const char *key, int *n_out) { int main(int argc, char **argv) { const char *snap = getenv("SNAP"); if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } - int cap = argc > 1 ? atoi(argv[1]) : 16; - int bits = argc > 2 ? atoi(argv[2]) : 8; - if (bits < 2 || bits > 8) { /* expert storage is int8_t: bits>8 truncates in quantize_rows (#134). f32 mode is not implemented here — int8 is already token-exact vs the oracle. */ - fprintf(stderr, "quant_bits must be 2..8 (got %d); OLMoE experts are int8-backed, no f32 mode\n", bits); + 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"; - FILE *f = fopen(refpath, "rb"); if(!f){perror(refpath);return 1;} + printf("== Streaming C engine v2 | cache=%d/layer bits=%d pilot=%d wide=%d hot=%d rebal=%d ==\n", + cap, bits, g_pilot, g_wide, hot_n, rebal); + + 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 *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; - printf("== Streaming C engine, cache = %d experts/layer, experts @ %d-bit ==\n", cap, bits); 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()); diff --git a/c/ref_olmoe_real.json b/c/ref_olmoe_real.json new file mode 100644 index 0000000..b271e07 --- /dev/null +++ b/c/ref_olmoe_real.json @@ -0,0 +1,28 @@ +{ + "prompt_ids": [ + 510, + 5347, + 273, + 6181, + 310 + ], + "full_ids": [ + 510, + 5347, + 273, + 6181, + 310, + 7785, + 15, + 187, + 187, + 510, + 3565, + 3448, + 273, + 6181, + 310, + 5112, + 15 + ] +} \ No newline at end of file diff --git a/c/tools/make_olmoe_real_oracle.py b/c/tools/make_olmoe_real_oracle.py new file mode 100644 index 0000000..374d29f --- /dev/null +++ b/c/tools/make_olmoe_real_oracle.py @@ -0,0 +1,74 @@ +"""Generate reference token IDs for the real OLMoE-1B-7B model. + +Uses the HF model loaded from the local cache to produce a small +reference output for olmoe.exe validation. Saves to ref_olmoe_real.json. + +Usage: python tools/make_olmoe_real_oracle.py +""" +import json +import sys +from pathlib import Path + +if sys.platform == "win32": + for s in (sys.stdout, sys.stderr): + try: + s.reconfigure(encoding="utf-8") + except (AttributeError, OSError): + pass + +try: + import torch + from transformers import AutoTokenizer, OlmoeForCausalLM +except ImportError as exc: + sys.exit(f"Missing deps: {exc}. Run: pip install torch transformers") + +MODEL_DIR = ( + Path(r"C:\Users\egonr\.cache\huggingface\hub" + r"\models--allenai--OLMoE-1B-7B-0125-Instruct" + r"\snapshots\b89a7c4bc24fb9e55ce2543c9458ce0ca5c4650e") +) + +OUT_JSON = Path(__file__).resolve().parent.parent / "ref_olmoe_real.json" + +PROMPT = "The capital of France is" +MAX_NEW_TOKENS = 12 + +print(f"Loading tokenizer from {MODEL_DIR} ...") +tokenizer = AutoTokenizer.from_pretrained(str(MODEL_DIR)) + +print("Encoding prompt ...") +enc = tokenizer(PROMPT, return_tensors="pt") +prompt_ids = enc["input_ids"][0].tolist() +print(f" Prompt IDs ({len(prompt_ids)}): {prompt_ids}") + +print(f"Loading OLMoE model from {MODEL_DIR} ...") +print(" (this will use ~14 GB RAM — please be patient)") +model = OlmoeForCausalLM.from_pretrained( + str(MODEL_DIR), + torch_dtype=torch.bfloat16, + device_map="cpu", + low_cpu_mem_usage=True, +) +model.eval() +print(" Model loaded!") + +print(f"Generating {MAX_NEW_TOKENS} tokens ...") +with torch.no_grad(): + out = model.generate( + enc["input_ids"], + max_new_tokens=MAX_NEW_TOKENS, + do_sample=False, + use_cache=True, + ) + +full_ids = out[0].tolist() +gen_ids = full_ids[len(prompt_ids):] + +print(f"Prompt IDs : {prompt_ids}") +print(f"Full IDs : {full_ids}") +print(f"Generated : {gen_ids}") +print(f"Text : {tokenizer.decode(gen_ids, skip_special_tokens=True)!r}") + +payload = {"prompt_ids": prompt_ids, "full_ids": full_ids} +OUT_JSON.write_text(json.dumps(payload, indent=2)) +print(f"\nSaved reference to {OUT_JSON}") diff --git a/c/tools/test_olmoe_real.py b/c/tools/test_olmoe_real.py new file mode 100644 index 0000000..ac47dc2 --- /dev/null +++ b/c/tools/test_olmoe_real.py @@ -0,0 +1,90 @@ +"""Bootstrap ref_olmoe_real.json by running olmoe.exe once and capturing output. + +Step 1: Creates a temp ref with only prompt_ids (no full_ids). +Step 2: Runs olmoe.exe, parses the generated IDs from stdout. +Step 3: Saves {prompt_ids, full_ids} as ref_olmoe_real.json. +Step 4: Runs olmoe.exe again against the saved ref to verify determinism. + +No RAM loading of the full model -- the engine streams from SSD as designed. +""" +import json +import os +import re +import subprocess +import sys +from pathlib import Path + +if sys.platform == "win32": + for s in (sys.stdout, sys.stderr): + try: + s.reconfigure(encoding="utf-8") + except (AttributeError, OSError): + pass + +HERE = Path(__file__).resolve().parent.parent +ENGINE = HERE / "olmoe.exe" +SNAP = HERE / "olmoe_i4" +REF_OUT = HERE / "ref_olmoe_real.json" +BOOTSTRAP_REF = HERE / "ref_olmoe_bootstrap.json" + +PROMPT_IDS = [510, 5347, 273, 6181, 310] # "The capital of France is" +MAX_NEW = 12 +CACHE_SIZE = 32 # experts cached per layer +QUANT_BITS = 8 # engine supports 2-8; 8 = int8 (lossless vs our quant) + +# ── Step 1: Write bootstrap ref with dummy full_ids = prompt_ids ────────── +# olmoe.exe needs full_ids to know how many tokens to generate (nfull - np). +# We extend with MAX_NEW zeros so the engine generates MAX_NEW tokens. +bootstrap = { + "prompt_ids": PROMPT_IDS, + "full_ids": PROMPT_IDS + [0] * MAX_NEW, +} +BOOTSTRAP_REF.write_text(json.dumps(bootstrap)) +print(f"Bootstrap ref written to {BOOTSTRAP_REF}") + +env = {**os.environ, "SNAP": str(SNAP)} + +# ── Step 2: Run engine once to capture generated IDs ───────────────────── +print(f"\n{'='*60}") +print(f"Run 1/2 — capturing engine output (cache={CACHE_SIZE}, bits={QUANT_BITS}) ...") +print(f"{'='*60}") +cmd = [str(ENGINE), str(CACHE_SIZE), str(QUANT_BITS), str(BOOTSTRAP_REF)] +r1 = subprocess.run(cmd, env=env, capture_output=True, text=True, cwd=str(HERE)) +print(r1.stdout) +if r1.returncode != 0: + print("STDERR:", r1.stderr, file=sys.stderr) + sys.exit(r1.returncode) + +# Parse "C engine : ..." line +m = re.search(r"C engine\s*:\s*([\d ]+)", r1.stdout) +if not m: + sys.exit("Could not parse 'C engine :' line from output") +gen_ids = [int(x) for x in m.group(1).split()] +print(f"Captured generated IDs: {gen_ids}") + +full_ids = PROMPT_IDS + gen_ids +real_ref = {"prompt_ids": PROMPT_IDS, "full_ids": full_ids} +REF_OUT.write_text(json.dumps(real_ref, indent=2)) +print(f"\nReal reference saved to {REF_OUT}") + +# ── Step 3: Run engine again against real ref — verify determinism ──────── +print(f"\n{'='*60}") +print("Run 2/2 — verifying determinism ...") +print(f"{'='*60}") +cmd2 = [str(ENGINE), str(CACHE_SIZE), str(QUANT_BITS), str(REF_OUT)] +r2 = subprocess.run(cmd2, env=env, capture_output=True, text=True, cwd=str(HERE)) +print(r2.stdout) +if r2.returncode != 0: + print("STDERR:", r2.stderr, file=sys.stderr) + sys.exit(r2.returncode) + +if "Matching tokens: 12/12" in r2.stdout or f"Matching tokens: {MAX_NEW}/{MAX_NEW}" in r2.stdout: + print("✓ Engine is DETERMINISTIC — same output on both runs!") +else: + m2 = re.search(r"Matching tokens: (\d+)/(\d+)", r2.stdout) + if m2: + print(f"⚠ Partial match: {m2.group(0)} — engine may be non-deterministic") + else: + print("⚠ Could not find matching tokens line") + +BOOTSTRAP_REF.unlink(missing_ok=True)