From a0b929c153052c61d200566085c0e126c23947ef Mon Sep 17 00:00:00 2001 From: Doug Sharp Date: Tue, 21 Jul 2026 10:42:04 +0100 Subject: [PATCH 01/96] Metal: chunk coli_metal_gemm to fix grid-size truncation at large prefill Large dense GEMMs (NT=S*O elements) were dispatched as a single grid; past the device threadgroup-grid limit the tail output rows were silently never computed, leaving stale scratch -> nondeterministic long-context corruption under COLI_METAL=1 (identical prompt correct on CPU / at short lengths). Chunk the dispatch to <=2^25 elements via g_gx/g_gy buffer offsets: no kernel change, disjoint output ranges, negligible overhead, no-op for small S and decode. Gated COLI_GEMM_CHUNK (default on; =0 restores old path for A/B). Adds tests/test_gemm_largebatch.mm (make gemm-test): kv_b S=7478 nerr=1.0 before, clean after. --- c/Makefile | 6 ++ c/backend_metal.mm | 27 ++++++-- c/tests/test_gemm_largebatch.mm | 106 ++++++++++++++++++++++++++++++++ 3 files changed, 134 insertions(+), 5 deletions(-) create mode 100644 c/tests/test_gemm_largebatch.mm diff --git a/c/Makefile b/c/Makefile index 1799b6b5..f626652d 100644 --- a/c/Makefile +++ b/c/Makefile @@ -311,6 +311,12 @@ metal-test: tests/test_backend_metal.mm backend_metal.mm backend_metal.h $(METALXX) tests/test_backend_metal.mm backend_metal.mm -framework Metal -framework Foundation -o backend_metal_test ./backend_metal_test +# Standalone large-batch GEMM correctness sweep — reproduces the long-context prefill +# corruption in seconds (no model). OMP-parallel CPU reference. +gemm-test: tests/test_gemm_largebatch.mm backend_metal.mm backend_metal.h + $(METALXX) $(OMPC) tests/test_gemm_largebatch.mm backend_metal.mm $(OMPL) -framework Metal -framework Foundation -o gemm_largebatch_test + ./gemm_largebatch_test + cuda-test: backend_cuda.cu backend_cuda.h backend_gpu_compat.h tests/test_backend_cuda.cu @command -v "$(GPUCC)" >/dev/null 2>&1 || { echo "$(GPUCC_NAME) not found" >&2; exit 1; } "$(GPUCC)" $(GPUFLAGS) backend_cuda.cu tests/test_backend_cuda.cu -o backend_cuda_test$(EXE) diff --git a/c/backend_metal.mm b/c/backend_metal.mm index 6ec3fb4f..cc05106c 100644 --- a/c/backend_metal.mm +++ b/c/backend_metal.mm @@ -839,12 +839,29 @@ static bool resolve_attn(const AttnW *W, float *Lc, float *Rc, [e useResource:wb usage:MTLResourceUsageRead]; [e useResource:sb usage:MTLResourceUsageRead]; [e setComputePipelineState:g_gemv]; [e setBuffer:wb offset:woff atIndex:0]; [e setBuffer:sb offset:soff atIndex:1]; - [e setBuffer:g_gx offset:0 atIndex:2]; [e setBuffer:g_gy offset:0 atIndex:3]; - int NT=S*O; - [e setBytes:&S length:4 atIndex:4]; [e setBytes:&I length:4 atIndex:5]; + [e setBytes:&I length:4 atIndex:5]; [e setBytes:&O length:4 atIndex:6]; [e setBytes:&fmt length:4 atIndex:7]; - [e setBytes:&NT length:4 atIndex:8]; - [e dispatchThreadgroups:MTLSizeMake(((size_t)NT+3)/4,1,1) threadsPerThreadgroup:MTLSizeMake(128,1,1)]; + // Grid-size cap. One dispatch of NT=S*O output elements launches (NT+3)/4 threadgroups; past + // a device grid limit (observed on M-series: kv_b grid ~3.1e7 tg clean at S=4376, ~5.4e7 tg + // CORRUPT at S=7478) rows beyond the limit are silently never computed and the output keeps + // its prior contents -- fresh-zero standalone (nerr=1.0), stale scratch in-engine (the + // nondeterministic long-context corruption). Chunk over rows so each dispatch stays <=2^25 + // elements (grid <=2^23 tg, ~4x under the observed-clean bound). Chunks write disjoint g_gy + // ranges; g_gx/g_gy offsets are 16B-aligned (I,O multiples of 4). + // COLI_GEMM_CHUNK=0 disables chunking (one full dispatch = the buggy pre-fix behavior) so the + // fix can be A/B'd on a single binary. Default on. + static int chunk_on=-1; + if(chunk_on<0){ const char*e=getenv("COLI_GEMM_CHUNK"); chunk_on=(e&&e[0]=='0'&&!e[1])?0:1; } + const int64_t NT_MAX = chunk_on ? ((int64_t)1<<25) : ((int64_t)1<<62); + int CH=(int)(NT_MAX/O); if(CH<1) CH=1; if((int64_t)CH>S) CH=S; + for(int r0=0;r0 dense +// GEMM on GPU (`coli_metal_gemm`), independent of MoE. The engine's existing kernel tests +// only exercise S<=64, which is why the S>=~2400 fault was never caught. +// +// This sweeps S across the clean/corrupt bracket for a few real GLM matmul shapes, +// compares GPU vs CPU reference, prints the first diverging (row,col), AND runs the GPU +// twice to report determinism (race vs fixed compute error). +// +// Build/run: make gemm-test +#include "../backend_metal.h" +#include +#include +#include +#include +#include + +enum { I8=1, I4=2 }; + +// CPU reference: dequant->f32 MAC * per-row scale. OMP-parallel over output cols so the +// big shapes stay fast. +static void cpu_ref(int fmt, const void *W, const float *s, const float *x, + float *y, int S, int I, int O) { + const int8_t *q8 = (const int8_t*)W; const uint8_t *q4 = (const uint8_t*)W; + int rb4=(I+1)/2; + #pragma omp parallel for schedule(static) + for (int o=0;o>1)]; int v=(i&1)?(b>>4):(b&0xF); w=(float)(v-8); } + acc += w*xr[i]; + } + y[(size_t)si*O+o]=acc*s[o]; + } +} + +static size_t roundpg(size_t n){ size_t p=16384; return ((n+p-1)/p)*p; } + +// One (fmt,O,I,S) case: GPU vs CPU + GPU-vs-GPU determinism. Returns 0 = clean. +static int run_gemm(int fmt, int O, int I, int S, const char *name) { + int rb4=(I+1)/2; + size_t wn = (fmt==I8)?(size_t)O*I : (size_t)O*rb4; + size_t wb = roundpg(wn), sb = roundpg((size_t)O*4); + uint8_t *W=nullptr; float *Sc=nullptr; + posix_memalign((void**)&W,16384,wb); posix_memalign((void**)&Sc,16384,sb); + srand(1234 + S*7 + fmt); + for(size_t i=0;i x((size_t)S*I), yr((size_t)S*O), yg((size_t)S*O), yg2((size_t)S*O); + for(auto&v:x) v=((rand()%2000)-1000)/1000.f; + + cpu_ref(fmt, W, Sc, x.data(), yr.data(), S, I, O); + int ok1 = coli_metal_gemm(yg.data(), x.data(), W, Sc, fmt, S, I, O); + int ok2 = coli_metal_gemm(yg2.data(), x.data(), W, Sc, fmt, S, I, O); + + // GPU vs CPU: max normalized error + first diverging element. + double maxabs=0, ymax=0; long badidx=-1; + for(size_t i=0;i<(size_t)S*O;i++){ + double d=fabs((double)yg[i]-yr[i]); + if(d>maxabs) maxabs=d; + if(fabs(yr[i])>ymax) ymax=fabs(yr[i]); + if(badidx<0 && d > 1e-2*(fabs(yr[i])+1e-6)) badidx=(long)i; + } + double nerr = maxabs/(ymax+1e-9); + // GPU vs GPU: determinism. + long detdiff=0; for(size_t i=0;i<(size_t)S*O;i++) if(yg[i]!=yg2[i]) detdiff++; + + int clean = ok1 && ok2 && nerr < 1e-3; + printf(" %-26s S=%-5d O=%-6d I=%-5d nerr=%.2e det:%s %s", + name, S, O, I, nerr, + detdiff? "RACE(diff)" : "same", + clean? "ok" : "*** CORRUPT"); + if(badidx>=0) printf(" first-bad row=%ld col=%ld (gpu=%.4g cpu=%.4g)", + badidx/O, badidx%O, (double)yg[badidx], (double)yr[badidx]); + if(detdiff) printf(" [%ld/%zu gpu elems differ run-to-run]", detdiff, (size_t)S*O); + printf("\n"); + + coli_metal_unregister(W); coli_metal_unregister(Sc); free(W); free(Sc); + return clean?0:1; +} + +int main(void){ + if(!coli_metal_init()){ printf("Metal unavailable (skipping)\n"); return 0; } + printf("coli_metal_gemm large-batch sweep (GPU vs CPU, + determinism):\n"); + int fail=0; + // Real GLM-5.2 matmul shapes. kv_b (O=28672,I=512) has the largest S*O. + struct { int O,I; const char*n; } shapes[] = { + { 2048, 6144, "gate/up (O2048,I6144)" }, + { 6144, 2048, "down (O6144,I2048)" }, + { 28672, 512, "kv_b (O28672,I512)" }, + }; + int Ss[] = { 512, 2153, 4376, 7478 }; // clean control / clean bracket / corrupt bracket / worst + for(auto &sh : shapes){ + for(int S : Ss) fail |= run_gemm(I4, sh.O, sh.I, S, sh.n); + printf("\n"); + } + printf(fail? "GEMM large-batch: CORRUPTION DETECTED\n" : "GEMM large-batch: all clean\n"); + coli_metal_shutdown(); + return fail; +} From 7e707c7b51278b87808dd70ef0b80bbb9b2de116 Mon Sep 17 00:00:00 2001 From: Egon Ruiter Date: Wed, 22 Jul 2026 12:11:16 +0200 Subject: [PATCH 02/96] feat(olmoe): port LFRU prefetch eviction guard to OLMoE testbed --- c/olmoe.c | 49 ++++++++++++++++++++++++++++--------------------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/c/olmoe.c b/c/olmoe.c index d9460d40..74df155b 100644 --- a/c/olmoe.c +++ b/c/olmoe.c @@ -82,6 +82,7 @@ typedef struct { 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) */ + uint64_t *last_access; /* [n_layers * n_experts], clock time when expert was last accessed */ } Model; static pthread_mutex_t g_pilot_mx = PTHREAD_MUTEX_INITIALIZER; @@ -90,6 +91,13 @@ 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 int g_pilot_evict_guard = 1; /* PILOT_EVICT_GUARD=0 to disable LFRU prefetch eviction guard */ + +static uint64_t lfru_score(uint32_t heat, uint64_t last, uint64_t clock) { + uint64_t age = (clock > last) ? (clock - last) : 0; + uint64_t recent = (age < 255) ? (255 - age) : 0; + return ((uint64_t)heat << 8) | recent; +} static void pilot_prefetch(Model *m, int lnext, const float *x, int S); static void *pilot_worker(void *arg); @@ -182,25 +190,6 @@ static void matmul_q(float *y, const float *x, const int8_t *q, const float *sca } } -/* 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) { @@ -302,6 +291,7 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { 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)); + m->last_access = calloc((size_t)c->n_layers * c->n_experts, sizeof(uint64_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; @@ -402,6 +392,7 @@ static void expert_get(Model *m, int layer, int eid, Slot **out) { 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]; + if (m->last_access) m->last_access[layer * m->c.n_experts + eid] = m->clock; pthread_mutex_unlock(&g_pilot_mx); return; } @@ -440,6 +431,7 @@ static void expert_get(Model *m, int layer, int eid, Slot **out) { s->eid = eid; s->pinned = m->is_pinned[layer * c->n_experts + eid]; s->used = ++m->clock; + if (m->last_access) m->last_access[layer * c->n_experts + eid] = m->clock; *out = s; pthread_mutex_unlock(&g_pilot_mx); } @@ -719,6 +711,19 @@ static void pilot_realload(Model *m, int layer, int eid) { pthread_mutex_unlock(&g_pilot_mx); return; /* all pinned/in-flight, skip */ } + + /* LFRU eviction guard: don't displace a warm resident expert with a speculation */ + if (g_pilot_evict_guard && m->freq && m->last_access && lc->slots[lru].eid >= 0) { + int vid = lc->slots[lru].eid; + uint64_t vs = lfru_score(m->freq[layer * c->n_experts + vid], m->last_access[layer * c->n_experts + vid], m->clock); + uint64_t cs = lfru_score(m->freq[layer * c->n_experts + eid], m->last_access[layer * c->n_experts + eid], m->clock); + if (cs <= vs + (vs >> 2) + (4u << 8)) { + m->is_queued[layer * c->n_experts + eid] = 0; + pthread_mutex_unlock(&g_pilot_mx); + return; /* drop speculation */ + } + } + s = &lc->slots[lru]; s->pinned = 0; } s->eid = -1; s->used = ++m->clock; @@ -730,6 +735,7 @@ static void pilot_realload(Model *m, int layer, int eid) { s->eid = eid; s->pinned = m->is_pinned[layer * c->n_experts + eid]; s->used = ++m->clock; + if (m->last_access) m->last_access[layer * c->n_experts + eid] = m->clock; m->is_queued[layer * c->n_experts + eid] = 0; pthread_mutex_unlock(&g_pilot_mx); } @@ -939,6 +945,7 @@ int main(int argc, char **argv) { if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } g_pilot = getenv("PILOT") ? atoi(getenv("PILOT")) : 0; g_wide = getenv("WIDE") ? atoi(getenv("WIDE")) : 1; + g_pilot_evict_guard = getenv("PILOT_EVICT_GUARD") ? atoi(getenv("PILOT_EVICT_GUARD")) : 1; if (g_wide < 1) g_wide = 1; if (g_wide > 4) g_wide = 4; int hot_n = getenv("HOT") ? atoi(getenv("HOT")) : 0; @@ -953,8 +960,8 @@ int main(int argc, char **argv) { 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 smooth=%.2f conf=%.2f ==\n", - cap, bits, g_pilot, g_wide, hot_n, smooth, conf); + printf("== Streaming C engine v2.2 | cache=%d/layer bits=%d pilot=%d wide=%d guard=%d hot=%d smooth=%.2f conf=%.2f ==\n", + cap, bits, g_pilot, g_wide, g_pilot_evict_guard, hot_n, 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); From 70f06cdb4351045c41b50cb371f6975e7c43bb01 Mon Sep 17 00:00:00 2001 From: Doug Sharp Date: Wed, 22 Jul 2026 21:59:37 +0100 Subject: [PATCH 03/96] metal: clamp chunk size in 64-bit before narrowing (UB in the COLI_GEMM_CHUNK=0 path) --- c/backend_metal.mm | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/c/backend_metal.mm b/c/backend_metal.mm index cc05106c..2894d791 100644 --- a/c/backend_metal.mm +++ b/c/backend_metal.mm @@ -847,13 +847,21 @@ static bool resolve_attn(const AttnW *W, float *Lc, float *Rc, // its prior contents -- fresh-zero standalone (nerr=1.0), stale scratch in-engine (the // nondeterministic long-context corruption). Chunk over rows so each dispatch stays <=2^25 // elements (grid <=2^23 tg, ~4x under the observed-clean bound). Chunks write disjoint g_gy - // ranges; g_gx/g_gy offsets are 16B-aligned (I,O multiples of 4). + // ranges. Offset alignment is STRUCTURAL, not shape-dependent (holds for any I,O -- a tiny + // oracle checkpoint or a future container with odd dims, not just GLM's shapes): g_gy is only + // ever written scalar (y[row]), so r0*O*4 needs just 4B; and the 16B float4 loads on g_gx + // execute only inside loops gated by I8=(I&7)?0:(I/8), i.e. only when I%8==0, where r0*I*4 is + // a multiple of 32. With I%8!=0, I8=0, x4 is never dereferenced and x is read scalar via xr[i]. // COLI_GEMM_CHUNK=0 disables chunking (one full dispatch = the buggy pre-fix behavior) so the // fix can be A/B'd on a single binary. Default on. static int chunk_on=-1; if(chunk_on<0){ const char*e=getenv("COLI_GEMM_CHUNK"); chunk_on=(e&&e[0]=='0'&&!e[1])?0:1; } const int64_t NT_MAX = chunk_on ? ((int64_t)1<<25) : ((int64_t)1<<62); - int CH=(int)(NT_MAX/O); if(CH<1) CH=1; if((int64_t)CH>S) CH=S; + // Clamp in 64-bit BEFORE narrowing: with chunking off NT_MAX/O is ~1.6e14 for kv_b (O=28672), + // far past INT_MAX, so casting first is implementation-defined. Were it to truncate negative + // on some toolchain, CH=1 would make the disable path dispatch one row at a time and silently + // STOP reproducing the bug it exists to demonstrate. After the clamp CH <= S <= INT_MAX. + int64_t ch64=NT_MAX/O; if(ch64<1) ch64=1; if(ch64>S) ch64=S; int CH=(int)ch64; for(int r0=0;r0 Date: Wed, 22 Jul 2026 22:10:27 +0100 Subject: [PATCH 04/96] gemm-test: correct stale threshold comments (trigger is the dispatch grid NT=S*O, not S; S=4376 is clean) --- c/tests/test_gemm_largebatch.mm | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/c/tests/test_gemm_largebatch.mm b/c/tests/test_gemm_largebatch.mm index fbfa6a41..e2106811 100644 --- a/c/tests/test_gemm_largebatch.mm +++ b/c/tests/test_gemm_largebatch.mm @@ -3,7 +3,10 @@ // // Isolation established (see INVESTIGATION_LOG_metal_moe_race.md): corruption <=> dense // GEMM on GPU (`coli_metal_gemm`), independent of MoE. The engine's existing kernel tests -// only exercise S<=64, which is why the S>=~2400 fault was never caught. +// only exercise S<=64, which is why this fault was never caught. The trigger is the DISPATCH +// GRID (NT=S*O), not S alone: only the largest-O shape here (kv_b, O=28672) crosses the device +// limit, and only at S=7478 (~5.4e7 tg) -- it is still clean at S=4376 (~3.1e7 tg), and the +// smaller-O shapes (gate/up, down) stay clean at every S in the sweep. // // This sweeps S across the clean/corrupt bracket for a few real GLM matmul shapes, // compares GPU vs CPU reference, prints the first diverging (row,col), AND runs the GPU @@ -95,7 +98,7 @@ int main(void){ { 6144, 2048, "down (O6144,I2048)" }, { 28672, 512, "kv_b (O28672,I512)" }, }; - int Ss[] = { 512, 2153, 4376, 7478 }; // clean control / clean bracket / corrupt bracket / worst + int Ss[] = { 512, 2153, 4376, 7478 }; // control / clean / clean, just under the kv_b cliff / CORRUPT for kv_b for(auto &sh : shapes){ for(int S : Ss) fail |= run_gemm(I4, sh.O, sh.I, S, sh.n); printf("\n"); From b7d4835e3c9eac33c41036246c44aadc9b297a27 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 15 Jul 2026 23:09:25 -0400 Subject: [PATCH 05/96] inkling: Stage-A C engine for Thinking Machines Inkling (975B MoE), oracle-validated MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New standalone engine (olmoe.c pattern) for the Inkling text stack: GQA with interleaved sliding-window (512, 16 KV heads) / global (8 KV heads) attention, learned relative-position bias banks (no RoPE), tau log-length scaling on global layers, four depthwise causal short convs per layer (K/V, post-attn, post-MLP; fp32, residual inside, cached conv state for decode), dense + MoE layers, sigmoid router with loss-free bias top-k and jointly normalized routed+shared weights, mup logit scaling with unpadded-vocab slice. Routed experts stream per-expert from the fused [E,2I,D]/[E,D,I] tensors with an LRU cache; -p "prompt" mode does streaming greedy generation with eos stop. tools/make_tiny_inkling.py builds a tiny random-init oracle via HF transformers (>=5.14): the engine reproduces it exactly — 36/36 teacher-forced argmax, 24/24 greedy tokens. Co-Authored-By: Claude Fable 5 --- .gitignore | 1 + c/Makefile | 3 + c/inkling.c | 770 +++++++++++++++++++++++++++++++++++ c/tools/make_tiny_inkling.py | 81 ++++ 4 files changed, 855 insertions(+) create mode 100644 c/inkling.c create mode 100644 c/tools/make_tiny_inkling.py diff --git a/.gitignore b/.gitignore index f62b3792..b6866801 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,7 @@ c/colibri.exe c/glm c/glm.exe c/olmoe +c/inkling c/olmoe.exe c/iobench c/iobench.exe diff --git a/c/Makefile b/c/Makefile index 56d76f95..fbb68a1d 100644 --- a/c/Makefile +++ b/c/Makefile @@ -339,6 +339,9 @@ cuda-bench: backend_cuda.cu backend_cuda.h backend_gpu_compat.h tests/bench_tens olmoe$(EXE): olmoe.c st.h json.h compat.h $(CC) $(CFLAGS) olmoe.c -o olmoe$(EXE) $(LDFLAGS) +inkling$(EXE): inkling.c st.h json.h compat.h + $(CC) $(CFLAGS) inkling.c -o inkling$(EXE) $(LDFLAGS) + # Use a baseline that matches the compiler target. macOS already targets a # portable baseline when ARCH is empty; forcing the x86 value there breaks # Apple Silicon. Unknown targets use native rather than an invalid x86 flag. diff --git a/c/inkling.c b/c/inkling.c new file mode 100644 index 00000000..d87d3ccf --- /dev/null +++ b/c/inkling.c @@ -0,0 +1,770 @@ +/* Pure-C inference engine for Thinking Machines "Inkling" (text-only), Stage A. + * Goal, like olmoe.c before GLM-5.2: reproduce the EXACT token ids of the HF + * transformers reference (ref_inkling.json from tools/make_tiny_inkling.py) + * to validate the core math before scaling to the 975B checkpoint. + * + * Architecture (vs glm.c's MLA/RoPE/DSA — shares almost nothing): + * - hybrid attention: sliding-window layers (window=512, 16 KV heads) and + * global layers (8 KV heads) interleaved 5:1; conventional GQA, no RoPE + * - learned relative-position bias: r_proj(x) mixes a per-layer bank + * proj[d_rel, rel_extent] into one bias per backward distance + * - log-length scaling tau on global layers past n_floor tokens + * - depthwise-causal short convs (kernel 4, residual inside, fp32): + * on K and V inside attention, after attention, and after the MLP + * - MoE: sigmoid router + loss-free bias for top-k selection; combine + * weights are sigmoids of the raw logits jointly normalized over + * topk routed + n_shared shared experts, x route_scale x global_scale + * - logits: hidden / logits_mup_width_multiplier, sliced to unpadded vocab + * + * Dense weights (attn, norms, convs, router, shared experts, dense MLP) + * resident in RAM as f32; routed experts streamed from disk per-expert out + * of the fused [E, 2I, D] / [E, D, I] tensors, LRU-cached, optionally + * int-quantized (bits=0 keeps them f32 for bit-exact oracle validation). + */ +#define _GNU_SOURCE +#include +#include +#include +#include +#include +#if defined(__APPLE__) || defined(__linux__) || defined(__FreeBSD__) +#include +#endif +#include "st.h" +#include "tok.h" + +#define MAXL 256 + +/* ---------- config ---------- */ +typedef struct { + int hidden, n_layers, vocab, unpad_vocab; + int n_heads, n_kv, head_dim; /* global ("hybrid") layers */ + int swa_heads, swa_kv, swa_hd; /* sliding ("hybrid_sliding") layers */ + int window, d_rel, rel_extent, conv_k; + double log_floor; /* <=0: log scaling off */ + float log_alpha; + int n_experts, topk, n_shared, moe_inter, dense_inter; + int eos; + float eps, route_scale, mup; + unsigned char local[MAXL]; /* 1 = sliding-window layer */ + unsigned char sparse[MAXL]; /* 1 = MoE layer, 0 = dense MLP */ +} Cfg; + +/* per-layer dims that depend on the attention type */ +#define L_HEADS(c,i) ((c)->local[i] ? (c)->swa_heads : (c)->n_heads) +#define L_KV(c,i) ((c)->local[i] ? (c)->swa_kv : (c)->n_kv) +#define L_HD(c,i) ((c)->local[i] ? (c)->swa_hd : (c)->head_dim) +#define L_EXT(c,i) ((c)->local[i] ? (c)->window : (c)->rel_extent) + +/* ---------- resident per-layer weights (f32) ---------- */ +typedef struct { + float *in_ln, *post_ln; + float *q, *k, *v, *r, *o; /* projections */ + float *qn, *kn; /* per-head rmsnorm [head_dim] */ + float *relp; /* [d_rel, ext] bias bank */ + float *k_cw, *v_cw, *a_cw, *m_cw; /* sconv weights, [C*K] depthwise */ + /* dense layers */ + float *dg, *du, *dd, dgs; + /* MoE layers */ + float *router, *rbias, rgs; /* [E+ns, D], [E], scalar */ + float *sh_g, *sh_u, *sh_d; /* shared experts [ns][I,D] etc. */ +} Layer; + +/* ---------- routed-expert LRU cache ---------- */ +typedef struct { + int eid; uint64_t used; + int8_t *q13, *q2; float *s13, *s2; /* bits>0: int-quantized rows */ + float *f13, *f2; /* bits==0: raw f32 */ +} Slot; +typedef struct { Slot *slots; int n, cap; } LCache; + +typedef struct { + Cfg c; + shards S; + int quant_bits; /* 0 = f32 experts (oracle mode) */ + int xq; /* experts on disk are a colibri container (U8 + .qs) */ + float *embed, *embed_norm, *lm_head, *final_norm; + Layer *L; + LCache *cache; + uint64_t clock, hits, miss; + float **K, **V; int kv_len, max_t; /* per-layer [kv][max_t][hd] */ + float **cs[4]; /* conv states, [n_layers][C*(K-1)] */ + double dense_load_s; +} Model; + +/* ---------- 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); } +#else +static double rss_gb(void) { struct rusage r; getrusage(RUSAGE_SELF, &r); return r.ru_maxrss / (1024.0*1024.0); } +#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; } +static float sigmoidf(float x) { return 1.f / (1.f + expf(-x)); } +static float siluf(float x) { return x / (1.f + expf(-x)); } + +/* y[S,O] = x[S,I] @ W^T, W row-major [O,I] */ +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 @ q^T with per-row int quantization (same container math as olmoe.c) */ +static void matmul_q(float *y, const float *x, const int8_t *q, const float *scale, int I, int O) { + #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]; + } +} + +static void quantize_rows(const float *w, int8_t *q, float *scale, int O, int I, int bits) { + int qmax = (1 << (bits - 1)) - 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 computed in f64 accumulate like the f32->f32 reference */ +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; +} + +/* ---------- depthwise causal short conv, residual inside (fp32) ---------- + * seq[S,C] in-place: out[t] = sum_j w[c,j]*in[t+j-(K-1)] + in[t], history from + * state[C*(K-1)] (raw pre-conv inputs), which is updated to the new tail. */ +static void sconv_apply(float *seq, int S, int C, const float *w, float *state, int K) { + int P = K - 1; + #pragma omp parallel + { + float *col = malloc((P + S) * sizeof(float)); + #pragma omp for schedule(static) + for (int ch = 0; ch < C; ch++) { + for (int j = 0; j < P; j++) col[j] = state[(int64_t)ch*P + j]; + for (int t = 0; t < S; t++) col[P + t] = seq[(int64_t)t*C + ch]; + const float *wc = w + (int64_t)ch*K; + for (int t = 0; t < S; t++) { + float acc = 0.f; + for (int j = 0; j < K; j++) acc += wc[j] * col[t + j]; + seq[(int64_t)t*C + ch] = acc + col[P + t]; + } + for (int j = 0; j < P; j++) state[(int64_t)ch*P + j] = col[S + j]; + } + free(col); + } +} + +/* ---------- config loading ---------- + * Accepts both the flat text config (tiny oracle via InklingForCausalLM) and + * the full multimodal config.json (real checkpoint, fields under text_config). */ +static double jnum(jval *o, const char *k, double dflt) { + jval *v = json_get(o, k); + return (v && v->t == J_NUM) ? v->num : dflt; +} + +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 *root = json_parse(buf, &arena); + jval *r = json_get(root, "text_config"); if (!r) r = root; + + c->hidden = (int)jnum(r,"hidden_size",6144); + c->n_layers = (int)jnum(r,"num_hidden_layers",66); + c->vocab = (int)jnum(r,"vocab_size",201024); + c->unpad_vocab = (int)jnum(r,"unpadded_vocab_size",c->vocab); + c->n_heads = (int)jnum(r,"num_attention_heads",64); + c->n_kv = (int)jnum(r,"num_key_value_heads",8); + c->head_dim = (int)jnum(r,"head_dim",128); + c->swa_heads = (int)jnum(r,"swa_num_attention_heads",c->n_heads); + c->swa_kv = (int)jnum(r,"swa_num_key_value_heads",16); + c->swa_hd = (int)jnum(r,"swa_head_dim",c->head_dim); + c->window = (int)jnum(r,"sliding_window_size",512); + c->d_rel = (int)jnum(r,"d_rel",16); + c->rel_extent = (int)jnum(r,"rel_extent",1024); + c->log_floor = jnum(r,"log_scaling_n_floor",0); + c->log_alpha = (float)jnum(r,"log_scaling_alpha",0.1); + c->conv_k = (int)jnum(r,"sconv_kernel_size", jnum(r,"conv_kernel_size",4)); + c->n_experts = (int)jnum(r,"n_routed_experts",256); + c->topk = (int)jnum(r,"num_experts_per_tok",6); + c->n_shared = (int)jnum(r,"n_shared_experts",2); + c->eps = (float)jnum(r,"rms_norm_eps",1e-6); + c->route_scale = (float)jnum(r,"route_scale",8.0); + c->mup = (float)jnum(r,"logits_mup_width_multiplier",24.0); + /* eos lives at the top level in the real multimodal config, in the text + * config for a flat snapshot; may be null (tiny oracle) */ + jval *eo = json_get(root,"eos_token_id"); + if (!eo || eo->t != J_NUM) eo = json_get(r,"eos_token_id"); + c->eos = (eo && eo->t == J_NUM) ? (int)eo->num : -1; + /* real config.json: intermediate_size = MoE, dense_intermediate_size = dense. + * HF-saved config (post_init applied): intermediate_size = dense, moe_intermediate_size = MoE. */ + jval *dis = json_get(r,"dense_intermediate_size"); + if (dis && dis->t == J_NUM) { + c->dense_inter = (int)dis->num; + c->moe_inter = (int)jnum(r,"intermediate_size",3072); + } else { + c->dense_inter = (int)jnum(r,"intermediate_size",24576); + c->moe_inter = (int)jnum(r,"moe_intermediate_size",3072); + } + if (c->n_layers > MAXL) { fprintf(stderr,"n_layers %d > MAXL\n", c->n_layers); exit(1); } + + /* attention layer types: explicit layer_types[] > local_layer_ids[] > (i+1)%6 rule */ + jval *lt = json_get(r,"layer_types"); + jval *ll = json_get(r,"local_layer_ids"); + for (int i = 0; i < c->n_layers; i++) { + if (lt && lt->t == J_ARR) c->local[i] = (strcmp(lt->kids[i]->str,"hybrid_sliding")==0); + else if (ll && ll->t == J_ARR) { + c->local[i] = 0; + for (int j = 0; j < ll->len; j++) if ((int)ll->kids[j]->num == i) { c->local[i] = 1; break; } + } else c->local[i] = ((i + 1) % 6) != 0; + } + /* MLP types: explicit mlp_layer_types[] > dense_mlp_idx (first k layers dense) */ + jval *mt = json_get(r,"mlp_layer_types"); + int dense_idx = (int)jnum(r,"dense_mlp_idx",0); + for (int i = 0; i < c->n_layers; i++) { + if (mt && mt->t == J_ARR) c->sparse[i] = (strcmp(mt->kids[i]->str,"sparse")==0); + else c->sparse[i] = (i >= dense_idx); + } + free(buf); free(arena); +} + +/* ---------- weight loading ---------- */ +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); + return p; +} +static float load_scalar(Model *m, const char *name, float dflt) { + if (!st_has(&m->S, name)) return dflt; + float v; st_read_f32(&m->S, name, &v, 0); return v; +} + +/* f32 slice of a (possibly bf16/f16) tensor: element offset + count. + * Needed to stream one expert out of the fused [E,2I,D]/[E,D,I] tensors. */ +static void read_f32_slice(shards *S, const char *name, float *out, int64_t off, int64_t cnt) { + st_tensor *t = st_find(S, name); + if (!t) { fprintf(stderr, "missing tensor: %s\n", name); exit(1); } + if (t->dtype == 3) { fprintf(stderr, "%s: U8 container has no f32 view\n", name); exit(1); } + int esz = (t->dtype == 2) ? 4 : 2; + void *raw = malloc((size_t)cnt * esz); + if (!raw) { fprintf(stderr,"OOM slice %s\n",name); exit(1); } + if (pread(t->fd, raw, (size_t)cnt*esz, t->off + off*esz) != (ssize_t)(cnt*esz)) { perror("pread slice"); exit(1); } + if (t->dtype == 2) memcpy(out, raw, (size_t)cnt*4); + else if (t->dtype == 0) { uint16_t *p = raw; for (int64_t i = 0; i < cnt; i++) out[i] = bf16_to_f32(p[i]); } + else { uint16_t *p = raw; for (int64_t i = 0; i < cnt; i++) out[i] = f16_to_f32(p[i]); } + free(raw); + posix_fadvise(t->fd, t->off + off*esz, cnt*esz, POSIX_FADV_DONTNEED); +} + +/* raw byte slice of a U8 container tensor */ +static void read_u8_slice(shards *S, const char *name, uint8_t *out, int64_t boff, int64_t nb) { + st_tensor *t = st_find(S, name); + if (!t) { fprintf(stderr, "missing tensor: %s\n", name); exit(1); } + if (pread(t->fd, out, (size_t)nb, t->off + boff) != (ssize_t)nb) { perror("pread u8 slice"); exit(1); } + posix_fadvise(t->fd, t->off + boff, nb, POSIX_FADV_DONTNEED); +} + +/* container rows -> int8: rowb==cols is int8 verbatim; rowb==cols/2 is packed + * int4 (low nibble = even column, offset +8 — convert_inkling_int4.py / glm.c) */ +static void unpack_rows(const uint8_t *raw, int8_t *q, int64_t rows, int64_t cols, int64_t rowb) { + if (rowb == cols) { memcpy(q, raw, (size_t)(rows*cols)); return; } + if (rowb*2 != cols) { fprintf(stderr, "container row size %ld vs cols %ld unsupported\n", (long)rowb, (long)cols); exit(1); } + for (int64_t r = 0; r < rows; r++) { + const uint8_t *b = raw + r*rowb; + int8_t *qr = q + r*cols; + for (int64_t j = 0; j < rowb; j++) { + qr[2*j] = (int8_t)((b[j] & 0xF) - 8); + qr[2*j+1] = (int8_t)((b[j] >> 4) - 8); + } + } +} + +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; + int D = c->hidden, K = c->conv_k; + double t0 = now_s(); + m->embed = load_t(m, "model.embed_tokens.weight"); + m->embed_norm = st_has(&m->S,"model.embed_norm.weight") ? load_t(m,"model.embed_norm.weight") : NULL; + m->final_norm = load_t(m, "model.norm.weight"); + m->lm_head = load_t(m, "lm_head.weight"); + m->L = calloc(c->n_layers, sizeof(Layer)); + char nm[320]; + 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(r, "self_attn.r_proj.weight"); + LD(o, "self_attn.o_proj.weight"); + LD(qn,"self_attn.q_norm.weight"); LD(kn,"self_attn.k_norm.weight"); + LD(relp, "self_attn.rel_logits_proj.proj"); + LD(k_cw, "self_attn.k_sconv.conv1d.weight"); + LD(v_cw, "self_attn.v_sconv.conv1d.weight"); + LD(a_cw, "attn_sconv.conv1d.weight"); + LD(m_cw, "mlp_sconv.conv1d.weight"); + if (!c->sparse[i]) { + LD(dg, "mlp.gate_proj.weight"); LD(du, "mlp.up_proj.weight"); LD(dd, "mlp.down_proj.weight"); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.global_scale",i); l->dgs = load_scalar(m,nm,1.f); + } else { + LD(router, "mlp.gate.weight"); + LD(rbias, "mlp.gate.e_score_correction_bias"); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.gate.global_scale",i); l->rgs = load_scalar(m,nm,1.f); + LD(sh_g, "mlp.shared_experts.gate_proj"); + LD(sh_u, "mlp.shared_experts.up_proj"); + LD(sh_d, "mlp.shared_experts.down_proj"); + } + #undef LD + /* conv states: raw inputs of the previous K-1 steps, zero-init */ + int kvdim = L_KV(c,i) * L_HD(c,i); + for (int j = 0; j < 4; j++) { + if (!m->cs[j]) m->cs[j] = calloc(c->n_layers, sizeof(float*)); + int C = (j < 2) ? kvdim : D; + m->cs[j][i] = calloc((int64_t)C * (K-1), sizeof(float)); + } + } + 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)); } + /* container detection: converted snapshots store experts as U8 + .qs */ + for (int i = 0; i < c->n_layers; i++) if (c->sparse[i]) { + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",i); + st_tensor *t = st_find(&m->S, nm); + if (t) m->xq = (t->dtype == 3); + break; + } + m->dense_load_s = now_s() - t0; +} + +/* ---------- routed-expert fetch: slice of fused tensors, LRU-cached ---------- */ +static void expert_get(Model *m, int layer, int eid, Slot **out) { + LCache *lc = &m->cache[layer]; + 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->miss++; + Cfg *c = &m->c; + int64_t D = c->hidden, I = c->moe_inter; + int64_t n13 = 2*I*D, n2 = D*I; + Slot *s; + if (lc->n < lc->cap) { + s = &lc->slots[lc->n++]; + if (m->quant_bits || m->xq) { + s->q13 = malloc(n13); s->q2 = malloc(n2); + s->s13 = falloc(2*I); s->s2 = falloc(D); + } else { s->f13 = falloc(n13); s->f2 = falloc(n2); } + } 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]; } + char nm[320], qs[340]; + if (m->xq) { + /* colibri container: packed U8 rows + f32 row scales, raw read, no requant */ + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); + st_tensor *t = st_find(&m->S, nm); + int64_t rowb = t->nbytes / ((int64_t)c->n_experts * 2*I); /* D = int8, D/2 = int4 */ + uint8_t *raw = malloc((size_t)(2*I*rowb)); + read_u8_slice(&m->S, nm, raw, (int64_t)eid*2*I*rowb, 2*I*rowb); + unpack_rows(raw, s->q13, 2*I, D, rowb); + snprintf(qs,sizeof(qs),"%s.qs",nm); + read_f32_slice(&m->S, qs, s->s13, (int64_t)eid*2*I, 2*I); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); + t = st_find(&m->S, nm); + rowb = t->nbytes / ((int64_t)c->n_experts * D); + raw = realloc(raw, (size_t)(D*rowb)); + read_u8_slice(&m->S, nm, raw, (int64_t)eid*D*rowb, D*rowb); + unpack_rows(raw, s->q2, D, I, rowb); + snprintf(qs,sizeof(qs),"%s.qs",nm); + read_f32_slice(&m->S, qs, s->s2, (int64_t)eid*D, D); + free(raw); + } else if (m->quant_bits) { + float *tmp = falloc(n13 > n2 ? n13 : n2); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); + read_f32_slice(&m->S, nm, tmp, (int64_t)eid*n13, n13); + quantize_rows(tmp, s->q13, s->s13, 2*I, D, m->quant_bits); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); + read_f32_slice(&m->S, nm, tmp, (int64_t)eid*n2, n2); + quantize_rows(tmp, s->q2, s->s2, D, I, m->quant_bits); + free(tmp); + } else { + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); + read_f32_slice(&m->S, nm, s->f13, (int64_t)eid*n13, n13); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); + read_f32_slice(&m->S, nm, s->f2, (int64_t)eid*n2, n2); + } + s->eid = eid; s->used = ++m->clock; + *out = s; +} + +/* ---------- attention (GQA + sliding/global + relative bias + K/V sconv) ---------- */ +static void attention(Model *m, Layer *l, int li, float *x, int S, int pos0, float *out) { + Cfg *c = &m->c; + int D = c->hidden, H = L_HEADS(c,li), KV = L_KV(c,li), hd = L_HD(c,li), ext = L_EXT(c,li); + int local = c->local[li]; + int qdim = H*hd, kvdim = KV*hd, group = H/KV; + float *q = falloc((int64_t)S*qdim); + float *k = falloc((int64_t)S*kvdim); + float *vv = falloc((int64_t)S*kvdim); + float *rr = falloc((int64_t)S*H*c->d_rel); + matmul(q, x, l->q, S, D, qdim); + matmul(k, x, l->k, S, D, kvdim); + matmul(vv, x, l->v, S, D, kvdim); + matmul(rr, x, l->r, S, D, H*c->d_rel); + /* short convs on K and V (sequence-wise, over the raw projections) */ + sconv_apply(k, S, kvdim, l->k_cw, m->cs[0][li], c->conv_k); + sconv_apply(vv, S, kvdim, l->v_cw, m->cs[1][li], c->conv_k); + /* per-head q/k rmsnorm (scaling below is 1/hd, not 1/sqrt(hd), because of this) */ + for (int s = 0; s < S; s++) { + for (int h = 0; h < H; h++) rmsnorm_row(q + (int64_t)s*qdim + h*hd, q + (int64_t)s*qdim + h*hd, l->qn, hd, c->eps); + for (int h = 0; h < KV; h++) rmsnorm_row(k + (int64_t)s*kvdim + h*hd, k + (int64_t)s*kvdim + h*hd, l->kn, hd, c->eps); + } + /* append K,V to the cache */ + for (int s = 0; s < S; s++) for (int h = 0; h < KV; h++) { + int t = pos0 + s; + memcpy(m->K[li] + ((int64_t)h*m->max_t + t)*hd, k + (int64_t)s*kvdim + h*hd, hd*sizeof(float)); + memcpy(m->V[li] + ((int64_t)h*m->max_t + t)*hd, vv + (int64_t)s*kvdim + h*hd, hd*sizeof(float)); + } + float scale = 1.f / (float)hd; + float *ctx = falloc((int64_t)S*qdim); + #pragma omp parallel + { + float *rl = malloc(ext * sizeof(float)); + float *sc = malloc((size_t)m->max_t * sizeof(float)); + #pragma omp for collapse(2) schedule(static) + for (int h = 0; h < H; h++) { + for (int s = 0; s < S; s++) { + int qpos = pos0 + s; + int t0 = local && qpos - c->window + 1 > 0 ? qpos - c->window + 1 : 0; + /* mix the relative-bias bank for this (token, head): rl[dist] */ + const float *rv = rr + (int64_t)s*H*c->d_rel + h*c->d_rel; + for (int e = 0; e < ext; e++) { + float acc = 0.f; + for (int d = 0; d < c->d_rel; d++) acc += rv[d] * l->relp[(int64_t)d*ext + e]; + rl[e] = acc; + } + /* tau: log-length scaling on global layers (f32, per query pos) */ + float tau = 1.f; + if (!local && c->log_floor > 0) { + double en = (double)(qpos + 1) / c->log_floor; + if (en > 1.0) tau = 1.f + c->log_alpha * (float)log(en); + } + const float *qv = q + (int64_t)s*qdim + h*hd; + const float *Kh = m->K[li] + ((int64_t)(h/group)*m->max_t)*hd; + for (int t = t0; t <= qpos; t++) { + const float *kv = Kh + (int64_t)t*hd; + float acc = 0.f; + for (int d = 0; d < hd; d++) acc += qv[d]*kv[d]; + int dist = qpos - t; + sc[t - t0] = tau * (acc*scale + (dist < ext ? rl[dist] : 0.f)); + } + int n = qpos - t0 + 1; + softmax_row(sc, n); + float *cx = ctx + (int64_t)s*qdim + h*hd; + for (int d = 0; d < hd; d++) cx[d] = 0.f; + const float *Vh = m->V[li] + ((int64_t)(h/group)*m->max_t)*hd; + for (int t = t0; t <= qpos; t++) { + const float *vrow = Vh + (int64_t)t*hd; + float a = sc[t - t0]; + for (int d = 0; d < hd; d++) cx[d] += a * vrow[d]; + } + } + } + free(rl); free(sc); + } + matmul(out, ctx, l->o, S, qdim, D); + free(q); free(k); free(vv); free(rr); free(ctx); +} + +/* ---------- dense MLP ---------- */ +static void dense_mlp(Model *m, Layer *l, float *x, int S, float *out) { + Cfg *c = &m->c; int D = c->hidden, I = c->dense_inter; + float *g = falloc((int64_t)S*I), *u = falloc((int64_t)S*I); + matmul(g, x, l->dg, S, D, I); + matmul(u, x, l->du, S, D, I); + for (int64_t i = 0; i < (int64_t)S*I; i++) g[i] = siluf(g[i]) * u[i]; + matmul(out, g, l->dd, S, I, D); + for (int64_t i = 0; i < (int64_t)S*D; i++) out[i] *= l->dgs; + free(g); free(u); +} + +/* ---------- MoE: sigmoid router + bias top-k, joint routed+shared weights ---------- */ +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->moe_inter, ns = c->n_shared; + int ET = E + ns; + float *logits = falloc((int64_t)S*ET); + matmul(logits, x, l->router, S, D, ET); + 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 *lg = logits + (int64_t)s*ET; + /* selection: sigmoid(routed) + correction bias, top-K */ + int idx[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;} + float ch = sigmoidf(lg[e]) + l->rbias[e]; + if (!taken && ch > bv) { bv = ch; best = e; } + } + idx[kk] = best; + } + /* combine weights: sigmoids of the raw logits of (topK routed + shared), + * normalized to sum 1 over all K+ns, x route_scale x gate.global_scale */ + float w[80]; float sum = 0.f; + for (int kk = 0; kk < K; kk++) { w[kk] = sigmoidf(lg[idx[kk]]); sum += w[kk]; } + for (int j = 0; j < ns; j++) { w[K+j] = sigmoidf(lg[E+j]); sum += w[K+j]; } + for (int kk = 0; kk < K+ns; kk++) w[kk] *= c->route_scale * l->rgs / sum; + const float *xs = x + (int64_t)s*D; + float *os = out + (int64_t)s*D; + for (int kk = 0; kk < K; kk++) { + Slot *e; expert_get(m, layer, idx[kk], &e); + int qm = m->quant_bits || m->xq; + if (qm) { + matmul_q(g, xs, e->q13, e->s13, D, I); /* gate rows */ + matmul_q(u, xs, e->q13 + (int64_t)I*D, e->s13 + I, D, I); /* up rows */ + } else { + matmul(g, xs, e->f13, 1, D, I); + matmul(u, xs, e->f13 + (int64_t)I*D, 1, D, I); + } + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + if (qm) matmul_q(hh, g, e->q2, e->s2, I, D); + else matmul(hh, g, e->f2, 1, I, D); + for (int d = 0; d < D; d++) os[d] += w[kk] * hh[d]; + } + /* shared experts: gamma inside (before down_proj is linear, so applied at the end) */ + for (int j = 0; j < ns; j++) { + matmul(g, xs, l->sh_g + (int64_t)j*I*D, 1, D, I); + matmul(u, xs, l->sh_u + (int64_t)j*I*D, 1, D, I); + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + matmul(hh, g, l->sh_d + (int64_t)j*D*I, 1, I, D); + for (int d = 0; d < D; d++) os[d] += w[K+j] * hh[d]; + } + } + free(logits); free(g); free(u); free(hh); +} + +/* ---------- one forward pass over S new tokens ---------- + * Returns malloc'd logits of the last token (unpadded vocab). If tf_out is + * non-NULL also writes the per-position argmax (teacher-forcing check). */ +static float *step(Model *m, const int *ids, int S, int pos0, int *tf_out) { + Cfg *c = &m->c; int D = c->hidden; + float *x = falloc((int64_t)S*D); + for (int s = 0; s < S; s++) { + if (m->embed_norm) rmsnorm_row(x + (int64_t)s*D, m->embed + (int64_t)ids[s]*D, m->embed_norm, D, c->eps); + else 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, pos0, tmp); + sconv_apply(tmp, S, D, l->a_cw, m->cs[2][i], c->conv_k); + for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j]; + 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); + if (c->sparse[i]) moe(m, l, i, nrm, S, tmp); + else dense_mlp(m, l, nrm, S, tmp); + sconv_apply(tmp, S, D, l->m_cw, m->cs[3][i], c->conv_k); + for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j]; + } + m->kv_len = pos0 + S; + float *last = falloc(D); + float *logit = falloc(c->unpad_vocab); + if (tf_out) { + for (int s = 0; s < S; s++) { + rmsnorm_row(last, x + (int64_t)s*D, m->final_norm, D, c->eps); + for (int d = 0; d < D; d++) last[d] /= c->mup; + matmul(logit, last, m->lm_head, 1, D, c->unpad_vocab); + int best = 0; for (int i = 1; i < c->unpad_vocab; i++) if (logit[i] > logit[best]) best = i; + tf_out[pos0 + s] = best; + } + } + rmsnorm_row(last, x + (int64_t)(S-1)*D, m->final_norm, D, c->eps); + for (int d = 0; d < D; d++) last[d] /= c->mup; + matmul(logit, last, m->lm_head, 1, D, c->unpad_vocab); + free(x); free(nrm); free(tmp); free(last); + return logit; +} + +static void state_reset(Model *m) { + Cfg *c = &m->c; + m->kv_len = 0; + for (int i = 0; i < c->n_layers; i++) { + int kvdim = L_KV(c,i) * L_HD(c,i); + for (int j = 0; j < 4; j++) + memset(m->cs[j][i], 0, (int64_t)((j < 2) ? kvdim : c->hidden) * (c->conv_k-1) * sizeof(float)); + } +} + +static void kv_alloc(Model *m, int max_t) { + Cfg *c = &m->c; + m->max_t = max_t; + 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)L_KV(c,i) * max_t * L_HD(c,i)); + m->V[i] = falloc((int64_t)L_KV(c,i) * max_t * L_HD(c,i)); + } +} + +/* greedy generation, olmoe.c-style */ +static void generate(Model *m, const int *prompt, int np, int n_new, int *out) { + for (int i = 0; i < np; i++) out[i] = prompt[i]; + float *logit = step(m, prompt, np, 0, NULL); + int len = np; + Cfg *c = &m->c; + for (int s = 0; s < n_new; s++) { + int best = 0; float bv = logit[0]; + for (int i = 1; i < c->unpad_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, NULL); + } +} + +/* ---------- interactive prompt mode: greedy, streaming, stop on eos ---------- */ +static void generate_stream(Model *m, Tok *T, const char *prompt, int n_new) { + Cfg *c = &m->c; + int cap = (int)strlen(prompt) + 16; + int *ids = malloc(cap * sizeof(int)); + int np = tok_encode(T, prompt, (int)strlen(prompt), ids, cap); + if (np <= 0) { fprintf(stderr, "empty prompt after tokenization\n"); return; } + kv_alloc(m, np + n_new + 8); + printf("[%d prompt tokens] %s", np, prompt); fflush(stdout); + double t0 = now_s(), t1 = 0; + float *logit = step(m, ids, np, 0, NULL); + int len = np; + char buf[512]; + for (int s = 0; s < n_new; s++) { + int best = 0; float bv = logit[0]; + for (int i = 1; i < c->unpad_vocab; i++) if (logit[i] > bv) { bv = logit[i]; best = i; } + free(logit); + if (s == 0) t1 = now_s(); + if (best == c->eos) { printf("\n[eos after %d tokens]", s); break; } + int nb = tok_decode(T, &best, 1, buf, sizeof(buf)-1); + buf[nb] = 0; fputs(buf, stdout); fflush(stdout); + int one = best; + len++; + if (s == n_new - 1) break; + logit = step(m, &one, 1, len - 1, NULL); + } + double dt = now_s() - t1; + int gen = len - np; + printf("\n[prefill %.1fs | %d tokens in %.1fs = %.2f tok/s | RSS %.1f GB]\n", + t1 - t0, gen, dt, gen > 1 ? (gen-1)/dt : 0.0, rss_gb()); + free(ids); +} + +/* ---------- ref_inkling.json harness ---------- */ +static int *read_int_array(jval *o, const char *key, int *n_out) { + jval *a = json_get(o, key); + if (!a || a->t != J_ARR) { *n_out = 0; return NULL; } + 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=\n"); return 1; } + /* flags: -p "prompt" [-n N] -> generate mode; positional: [cap] [bits] [ref.json] */ + const char *prompt = NULL, *refpath = "ref_inkling.json"; + int cap = 16, bits = 0, n_new = 256, npos = 0; + for (int i = 1; i < argc; i++) { + if (!strcmp(argv[i], "-p") && i+1 < argc) prompt = argv[++i]; + else if (!strcmp(argv[i], "-n") && i+1 < argc) n_new = atoi(argv[++i]); + else if (npos == 0) { cap = atoi(argv[i]); npos++; } + else if (npos == 1) { bits = atoi(argv[i]); npos++; } + else refpath = argv[i]; + } + if (bits && (bits < 2 || bits > 8)) { fprintf(stderr, "quant_bits must be 0 (f32) or 2..8\n"); return 1; } + + if (prompt) { + Model m; model_init(&m, snap, cap, bits); + printf("== Inkling C engine, %d layers, experts @ %s, cache %d/layer ==\n", + m.c.n_layers, m.xq ? "container" : bits ? "int" : "f32", cap); + char tkp[2048]; snprintf(tkp, sizeof(tkp), "%s/tokenizer.json", snap); + Tok T; tok_load(&T, tkp); + generate_stream(&m, &T, prompt, n_new); + return 0; + } + + 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, ntf; + int *pids = read_int_array(ref,"prompt_ids",&np); + int *full = read_int_array(ref,"full_ids",&nfull); + int *tfref = read_int_array(ref,"tf_pred",&ntf); + int ngen = nfull - np; + + Model m; model_init(&m, snap, cap, bits); + printf("== Inkling C engine (Stage A), cache = %d experts/layer, experts @ %s ==\n", + cap, m.xq ? "container (int4/int8 + .qs)" : bits ? "int (runtime quant)" : "f32"); + printf("cfg: D=%d L=%d V=%d(%d) heads=%d/%d kv=%d/%d hd=%d win=%d d_rel=%d ext=%d E=%d+%d topk=%d\n", + m.c.hidden, m.c.n_layers, m.c.vocab, m.c.unpad_vocab, m.c.n_heads, m.c.swa_heads, + m.c.n_kv, m.c.swa_kv, m.c.head_dim, m.c.window, m.c.d_rel, m.c.rel_extent, + m.c.n_experts, m.c.n_shared, m.c.topk); + printf("resident weights loaded in %.1fs | RSS: %.2f GB\n", m.dense_load_s, rss_gb()); + kv_alloc(&m, nfull + 8); + + /* pass 1: teacher-forced argmax over the full reference sequence */ + if (tfref && ntf == nfull) { + int *tf = malloc(nfull * sizeof(int)); + float *lg = step(&m, full, nfull, 0, tf); + free(lg); + int ok = 0; for (int i = 0; i < nfull; i++) ok += (tf[i] == tfref[i]); + printf("teacher-forced argmax: %d/%d match\n", ok, nfull); + free(tf); + state_reset(&m); + } + + /* pass 2: greedy generation, token-for-token vs the oracle */ + int *out = malloc(nfull * sizeof(int)); + double t = now_s(); + generate(&m, pids, np, ngen, out); + double dt = now_s() - t; + int match = 0; + printf("Reference: "); for (int i=np;i +""" +import json +import sys + +import torch + +try: + from transformers import InklingForCausalLM, InklingTextConfig +except ImportError: + sys.exit("transformers has no Inkling support: pip install -U transformers") + + +def main(): + out = sys.argv[1] if len(sys.argv) > 1 else "tiny_inkling" + torch.manual_seed(0) + + cfg = InklingTextConfig( + vocab_size=256, + unpadded_vocab_size=250, + hidden_size=64, + num_hidden_layers=8, + num_attention_heads=4, + num_key_value_heads=2, + head_dim=16, + swa_num_attention_heads=4, + swa_num_key_value_heads=4, + swa_head_dim=16, + sliding_window_size=8, + d_rel=8, + rel_extent=32, + log_scaling_n_floor=8, # small, so tau != 1 is exercised + log_scaling_alpha=0.1, + local_layer_ids=[0, 1, 2, 3, 4, 6, 7], # global layer at 5 + dense_mlp_idx=2, + dense_intermediate_size=96, + moe_intermediate_size=32, + n_routed_experts=8, + num_experts_per_tok=2, + n_shared_experts=2, + route_scale=2.0, + logits_mup_width_multiplier=4.0, + max_position_embeddings=4096, + eos_token_id=None, + ) + + model = InklingForCausalLM(cfg).eval().float() + + prompt = [7, 42, 199, 3, 88, 154, 21, 60, 9, 133, 77, 245] + ids = torch.tensor([prompt], dtype=torch.long) + n_new = 24 + + with torch.no_grad(): + gen = model.generate( + ids, max_new_tokens=n_new, do_sample=False, use_cache=True + ) + full = gen[0].tolist() + tf = model(torch.tensor([full], dtype=torch.long)).logits[0].argmax(-1).tolist() + + model.save_pretrained(out, safe_serialization=True) + ref = {"prompt_ids": prompt, "full_ids": full, "tf_pred": tf} + with open(f"{out}/ref_inkling.json", "w") as f: + json.dump(ref, f) + print(f"saved tiny model + ref_inkling.json to {out}/") + print("continuation:", full[len(prompt):]) + + +if __name__ == "__main__": + main() From b721a1e8884c584d667d5531639281cba05ab45d Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 15 Jul 2026 23:10:10 -0400 Subject: [PATCH 06/96] tools: Inkling bf16 -> colibri int4 converter, e2e-validated MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit convert_inkling_int4.py maps the TML-native checkpoint onto the HF names inkling.c loads, following transformers' conversion_mapping.py verbatim — including the critical detail that every fused w13 tensor stores gate/up rows INTERLEAVED (g0,u0,g1,...), so de-interleave = even rows then odd rows. Routed experts (95% of params) quantize to int4 + .qs per-row scales with packing bit-identical to convert_fp8_to_int4.py; norms/sconvs/rel-banks/router stay f32; everything else passes through as bf16. Resumable per shard (atomic rename), --watch converts while the download is still running, --xbits 0/4/8, audio/vision/MTP tensors skipped. inkling.c grew the matching container reader: U8 expert tensors are detected at load and streamed as packed int4/int8 rows + scales with no runtime requantization. --selftest-e2e fabricates a TML-named checkpoint from the tiny HF oracle snapshot (inverse mapping, re-fused and re-interleaved), converts it both ways, and the engine reproduces the oracle exactly in both passthrough and int4-container modes (36/36 tf, 24/24 greedy). Co-Authored-By: Claude Fable 5 --- c/tools/convert_inkling_int4.py | 401 ++++++++++++++++++++++++++++++++ 1 file changed, 401 insertions(+) create mode 100644 c/tools/convert_inkling_int4.py diff --git a/c/tools/convert_inkling_int4.py b/c/tools/convert_inkling_int4.py new file mode 100644 index 00000000..fb7985c2 --- /dev/null +++ b/c/tools/convert_inkling_int4.py @@ -0,0 +1,401 @@ +#!/usr/bin/env python3 +"""Convert the Thinking Machines Inkling BF16 checkpoint (TML-native tensor +names) into a colibri snapshot readable by inkling.c. + +Name mapping follows transformers' checkpoint_conversion_mapping for +inkling_mm_model verbatim (conversion_mapping.py), including the CRITICAL +detail that every fused w13 tensor stores gate/up rows INTERLEAVED +(g0,u0,g1,u1,...): de-interleaving = even rows first, then odd rows. +Quantization math is bit-identical to convert_fp8_to_int4.py / the C engine +(np.rint = lrintf, per-row symmetric scales, low nibble = even column). + +Output tensor policy (Stage A): + - routed experts (95% of the params) -> int4 (or --xbits) + `.qs` f32 row scales + - norms, sconv weights, rel-bias banks, router (+bias/scales) -> f32 + - everything else (attn projections, dense/shared MLP, embed, lm_head) + -> bf16 passthrough (st_read_f32 in the C engine converts on load) + - model.audio.* / model.visual.* / model.mtp.* -> skipped (text-only) + +Modes: + --indir DIR --outdir DIR [--watch] convert local shards (resumable per + shard; --watch polls while `hf download` is still running and exits + when every shard in model.safetensors.index.json is converted) + --selftest numpy-only unit tests (interleave, int4) + --selftest-e2e HFDIR OUTBASE build a fake TML-named checkpoint from a + tiny HF-format snapshot (tools/make_tiny_inkling.py), then convert it + with passthrough and int4 experts -> OUTBASE-pass/, OUTBASE-i4/ +""" +import argparse +import glob +import json +import os +import re +import shutil +import sys +import time + +import numpy as np +import torch +from safetensors import safe_open +from safetensors.torch import save_file + +# ---------- quantization (identical to convert_fp8_to_int4.py) ---------- + +def quant_int8_rows(w): + """w: f32 [O,I] -> (int8 bytes [O,I] viewed u8, f32 scales [O])""" + s = np.abs(w).max(axis=1, keepdims=True) / 127.0 + s[s < 1e-8] = 1e-8 + q = np.clip(np.rint(w / s), -128, 127).astype(np.int8) + return q.view(np.uint8), s[:, 0].astype(np.float32) + + +def quant_int4_rows(w): + """w: f32 [O,I], I even -> (packed u8 [O,I/2], f32 scales [O]). + Low nibble = even column, high nibble = odd column, offset +8.""" + O, I = w.shape + assert I % 2 == 0 + s = np.abs(w).max(axis=1, keepdims=True) / 7.0 + s[s < 1e-8] = 1e-8 + q = np.clip(np.rint(w / s), -8, 7).astype(np.int32) + lo = (q[:, 0::2] + 8).astype(np.uint8) + hi = (q[:, 1::2] + 8).astype(np.uint8) + return (lo | (hi << 4)), s[:, 0].astype(np.float32) + + +def deinterleave(t: torch.Tensor, dim: int) -> torch.Tensor: + """TML fused-w13 layout (g0,u0,g1,u1,...) -> grouped ([gate; up]). + Matches transformers' Interleave(dim) conversion op.""" + n = t.shape[dim] + idx = torch.cat([torch.arange(0, n, 2), torch.arange(1, n, 2)]) + return t.index_select(dim, idx) + + +def interleave(t: torch.Tensor, dim: int) -> torch.Tensor: + """Inverse of deinterleave (used only to fabricate test checkpoints).""" + n = t.shape[dim] + idx = torch.empty(n, dtype=torch.long) + idx[0::2] = torch.arange(0, n // 2) + idx[1::2] = torch.arange(n // 2, n) + return t.index_select(dim, idx) + + +# ---------- name mapping (TML native -> HF names inkling.c loads) ---------- + +SKIP_PREFIXES = ("model.audio.", "model.visual.", "model.mtp.") + +RENAMES = [ + (r"^model\.llm\.embed\.weight$", "model.embed_tokens.weight"), + (r"^model\.llm\.embed_norm\.weight$", "model.embed_norm.weight"), + (r"^model\.llm\.norm\.weight$", "model.norm.weight"), + (r"^model\.llm\.unembed\.weight$", "lm_head.weight"), + (r"^model\.llm\.layers\.", "model.layers."), +] +SUBS = [ + (r"\.attn\.wq_du\.", ".self_attn.q_proj."), + (r"\.attn\.wk_dv\.", ".self_attn.k_proj."), + (r"\.attn\.wv_dv\.", ".self_attn.v_proj."), + (r"\.attn\.wr_du\.", ".self_attn.r_proj."), + (r"\.attn\.wo_ud\.", ".self_attn.o_proj."), + (r"\.attn\.q_norm\.", ".self_attn.q_norm."), + (r"\.attn\.k_norm\.", ".self_attn.k_norm."), + (r"\.attn\.k_sconv\.weight$", ".self_attn.k_sconv.conv1d.weight"), + (r"\.attn\.v_sconv\.weight$", ".self_attn.v_sconv.conv1d.weight"), + (r"\.attn\.rel_logits_proj\.proj$", ".self_attn.rel_logits_proj.proj"), + (r"\.attn_sconv\.weight$", ".attn_sconv.conv1d.weight"), + (r"\.mlp_sconv\.weight$", ".mlp_sconv.conv1d.weight"), + (r"\.attn_norm\.weight$", ".input_layernorm.weight"), + (r"\.mlp_norm\.weight$", ".post_attention_layernorm.weight"), + (r"\.mlp\.gate\.bias$", ".mlp.gate.e_score_correction_bias"), + (r"\.mlp\.w2_md\.weight$", ".mlp.down_proj.weight"), + (r"\.mlp\.experts\.w2_weight$", ".mlp.experts.down_proj"), + (r"\.mlp\.shared_experts\.shared_w2_weight$", ".mlp.shared_experts.down_proj"), +] +# names that stay f32 in the container (small / numerically sensitive; +# the reference keeps every sconv in fp32) +F32_RE = re.compile( + r"(norm\.weight$|layernorm\.weight$|rel_logits_proj\.proj$|conv1d\.weight$" + r"|global_scale$|e_score_correction_bias$|\.mlp\.gate\.weight$)" +) + + +def map_name(name): + """Simple renames only (fused w13 tensors are handled by the shard loop). + Returns the mapped name, or None to skip.""" + if name.startswith(SKIP_PREFIXES): + return None + for pat, rep in RENAMES: + name = re.sub(pat, rep, name) + for pat, rep in SUBS: + name = re.sub(pat, rep, name) + return name + + +# ---------- per-shard conversion ---------- + +def quantize_expert_tensor(f, name, xbits, out, out_name): + """Chunked (per-expert) de-interleave/quantize of the fused 3D expert + tensors, so peak RAM stays at one expert slice, not one 19 GB tensor.""" + sl = f.get_slice(name) + E, R, C = sl.get_shape() # w13: [E,2I,D] (interleaved), w2: [E,D,I] + is_w13 = name.endswith("w13_weight") + if xbits == 4: + qs = np.empty((E * R, C // 2), np.uint8) + else: + qs = np.empty((E * R, C), np.uint8) + sc = np.empty(E * R, np.float32) + for e in range(E): + w = sl[e] # [R, C] bf16 + if is_w13: + w = deinterleave(w, 0) # -> [gate rows; up rows] + w = w.float().numpy() + q, s = quant_int4_rows(w) if xbits == 4 else quant_int8_rows(w) + qs[e * R:(e + 1) * R] = q + sc[e * R:(e + 1) * R] = s + out[out_name] = torch.from_numpy(qs.reshape(-1)) + out[out_name + ".qs"] = torch.from_numpy(sc) + + +def convert_shard(path, out, xbits): + with safe_open(path, framework="pt") as f: + for name in f.keys(): + if name.startswith(SKIP_PREFIXES): + continue + base = map_name(name) + if name.endswith(".mlp.experts.w13_weight"): + tgt = base.replace(".w13_weight", ".gate_up_proj") + if xbits: + quantize_expert_tensor(f, name, xbits, out, tgt) + else: + t = f.get_tensor(name) + out[tgt] = deinterleave(t, 1).contiguous() + continue + if name.endswith(".mlp.experts.w2_weight"): + tgt = map_name(name) + if xbits: + quantize_expert_tensor(f, name, xbits, out, tgt) + else: + out[tgt] = f.get_tensor(name) + continue + if name.endswith(".mlp.shared_experts.shared_w13_weight"): + t = deinterleave(f.get_tensor(name), 1) # [ns, 2I, D] -> grouped + half = t.shape[1] // 2 + pref = base.replace(".shared_w13_weight", "") + out[pref + ".gate_proj"] = t[:, :half].contiguous() + out[pref + ".up_proj"] = t[:, half:].contiguous() + continue + if name.endswith(".mlp.w13_dn.weight"): + t = deinterleave(f.get_tensor(name), 0) # [2I, D] -> grouped + half = t.shape[0] // 2 + pref = base.replace(".w13_dn.weight", "") + out[pref + ".gate_proj.weight"] = t[:half].contiguous() + out[pref + ".up_proj.weight"] = t[half:].contiguous() + continue + t = f.get_tensor(name) + if F32_RE.search(base): + out[base] = t.float() + else: + out[base] = t # bf16 passthrough + + +AUX_FILES = ["config.json", "tokenizer.json", "tokenizer_config.json", + "special_tokens_map.json", "chat_template.jinja"] + + +def shard_out_name(src): + m = re.search(r"model-(\d+)-of-\d+\.safetensors$", os.path.basename(src)) + return f"out-{m.group(1)}.safetensors" if m else \ + "out-" + os.path.basename(src) + + +def convert_dir(indir, outdir, xbits, watch=False, delete_src=False): + os.makedirs(outdir, exist_ok=True) + index_path = os.path.join(indir, "model.safetensors.index.json") + done_src = set() + while True: + shards = sorted(glob.glob(os.path.join(indir, "model-*.safetensors"))) + if not shards: + single = os.path.join(indir, "model.safetensors") + if os.path.exists(single): + shards = [single] + for sp in shards: + if sp in done_src: + continue + dst = os.path.join(outdir, shard_out_name(sp)) + if os.path.exists(dst): + done_src.add(sp) + continue + t0 = time.time() + out = {} + convert_shard(sp, out, xbits) + if out: + save_file(out, dst + ".tmp") + os.replace(dst + ".tmp", dst) # atomic: partial writes never count as done + else: # shard held only skipped (mm/mtp) tensors + open(dst, "wb").close() + done_src.add(sp) + src_gb = os.path.getsize(sp) / 1e9 + dst_gb = os.path.getsize(dst) / 1e9 + print(f"{os.path.basename(sp)} ({src_gb:.1f}G) -> " + f"{os.path.basename(dst)} ({dst_gb:.1f}G) in {time.time()-t0:.0f}s", + flush=True) + if delete_src: + os.remove(sp) + # completion check: every shard named in the index is converted + total = None + if os.path.exists(index_path): + wm = json.load(open(index_path))["weight_map"] + total = sorted(set(wm.values())) + if all(os.path.exists(os.path.join(outdir, shard_out_name(s))) for s in total): + break + elif shards and not watch: + break + if not watch: + break + time.sleep(60) + for fn in AUX_FILES: + src = os.path.join(indir, fn) + if os.path.exists(src): + shutil.copy(src, outdir) + print(f"conversion complete -> {outdir}") + + +# ---------- selftests ---------- + +def selftest(): + torch.manual_seed(0) + # 1) interleave round-trip + semantics + g = torch.arange(0, 12).reshape(6, 2).float() + u = torch.arange(100, 112).reshape(6, 2).float() + fused = interleave(torch.cat([g, u]), 0) + assert torch.equal(fused[0], g[0]) and torch.equal(fused[1], u[0]), \ + "interleave: row 0 must be gate0, row 1 up0" + got = deinterleave(fused, 0) + assert torch.equal(got[:6], g) and torch.equal(got[6:], u) + # 2) int4 pack/unpack round-trip against the C engine's convention + w = np.random.randn(16, 32).astype(np.float32) + q, s = quant_int4_rows(w) + lo = (q & 0x0F).astype(np.int32) - 8 + hi = ((q >> 4) & 0x0F).astype(np.int32) - 8 + deq = np.empty_like(w) + deq[:, 0::2] = lo + deq[:, 1::2] = hi + deq *= s[:, None] + ref = np.clip(np.rint(w / s[:, None]), -8, 7) * s[:, None] + assert np.array_equal(deq, ref), "int4 round-trip mismatch" + # 3) name mapping spot checks + cases = { + "model.llm.embed.weight": "model.embed_tokens.weight", + "model.llm.unembed.weight": "lm_head.weight", + "model.llm.layers.3.attn.wq_du.weight": "model.layers.3.self_attn.q_proj.weight", + "model.llm.layers.3.attn.k_sconv.weight": "model.layers.3.self_attn.k_sconv.conv1d.weight", + "model.llm.layers.3.attn_sconv.weight": "model.layers.3.attn_sconv.conv1d.weight", + "model.llm.layers.3.attn_norm.weight": "model.layers.3.input_layernorm.weight", + "model.llm.layers.3.mlp_norm.weight": "model.layers.3.post_attention_layernorm.weight", + "model.llm.layers.3.mlp.gate.bias": "model.layers.3.mlp.gate.e_score_correction_bias", + "model.llm.layers.3.mlp.gate.global_scale": "model.layers.3.mlp.gate.global_scale", + "model.llm.layers.3.mlp.experts.w2_weight": "model.layers.3.mlp.experts.down_proj", + "model.llm.layers.0.mlp.w2_md.weight": "model.layers.0.mlp.down_proj.weight", + "model.mtp.layers.0.input_proj.weight": None, + "model.audio.encoder.weight": None, + "model.visual.final_norm.weight": None, + } + for src, want in cases.items(): + got = map_name(src) + assert got == want, f"map_name({src}) = {got}, want {want}" + print("SELFTEST OK") + + +def selftest_e2e(hfdir, outbase): + """Fabricate a TML-named checkpoint from a tiny HF snapshot (inverse + mapping, w13 re-fused AND re-interleaved), then convert it back. Validates + the converter + the C engine's container reader end-to-end without the + real download. Tensors stay f32 so the oracle comparison is exact.""" + src = os.path.join(hfdir, "model.safetensors") + fake = outbase + "-tml" + os.makedirs(fake, exist_ok=True) + inv = {} + with safe_open(src, framework="pt") as f: + names = list(f.keys()) + tensors = {n: f.get_tensor(n) for n in names} + layers = {} + for n in names: + m = re.match(r"model\.layers\.(\d+)\.(.*)", n) + if m: + layers.setdefault(int(m.group(1)), {})[m.group(2)] = tensors[n] + for n, t in tensors.items(): + if n == "model.embed_tokens.weight": inv["model.llm.embed.weight"] = t + elif n == "model.embed_norm.weight": inv["model.llm.embed_norm.weight"] = t + elif n == "model.norm.weight": inv["model.llm.norm.weight"] = t + elif n == "lm_head.weight": inv["model.llm.unembed.weight"] = t + for li, lt in layers.items(): + p = f"model.llm.layers.{li}." + for hf, tml in [("self_attn.q_proj.weight", "attn.wq_du.weight"), + ("self_attn.k_proj.weight", "attn.wk_dv.weight"), + ("self_attn.v_proj.weight", "attn.wv_dv.weight"), + ("self_attn.r_proj.weight", "attn.wr_du.weight"), + ("self_attn.o_proj.weight", "attn.wo_ud.weight"), + ("self_attn.q_norm.weight", "attn.q_norm.weight"), + ("self_attn.k_norm.weight", "attn.k_norm.weight"), + ("self_attn.rel_logits_proj.proj", "attn.rel_logits_proj.proj"), + ("self_attn.k_sconv.conv1d.weight", "attn.k_sconv.weight"), + ("self_attn.v_sconv.conv1d.weight", "attn.v_sconv.weight"), + ("attn_sconv.conv1d.weight", "attn_sconv.weight"), + ("mlp_sconv.conv1d.weight", "mlp_sconv.weight"), + ("input_layernorm.weight", "attn_norm.weight"), + ("post_attention_layernorm.weight", "mlp_norm.weight"), + ("mlp.gate.weight", "mlp.gate.weight"), + ("mlp.gate.e_score_correction_bias", "mlp.gate.bias"), + ("mlp.gate.global_scale", "mlp.gate.global_scale"), + ("mlp.global_scale", "mlp.global_scale"), + ("mlp.down_proj.weight", "mlp.w2_md.weight"), + ("mlp.experts.down_proj", "mlp.experts.w2_weight"), + ("mlp.shared_experts.down_proj", "mlp.shared_experts.shared_w2_weight")]: + if hf in lt: + inv[p + tml] = lt[hf] + if "mlp.gate_proj.weight" in lt: # dense: fuse + interleave + inv[p + "mlp.w13_dn.weight"] = interleave( + torch.cat([lt["mlp.gate_proj.weight"], lt["mlp.up_proj.weight"]]), 0) + if "mlp.experts.gate_up_proj" in lt: # routed: re-interleave dim1 + inv[p + "mlp.experts.w13_weight"] = interleave(lt["mlp.experts.gate_up_proj"], 1) + if "mlp.shared_experts.gate_proj" in lt: + inv[p + "mlp.shared_experts.shared_w13_weight"] = interleave( + torch.cat([lt["mlp.shared_experts.gate_proj"], + lt["mlp.shared_experts.up_proj"]], dim=1), 1) + save_file({k: v.contiguous() for k, v in inv.items()}, + os.path.join(fake, "model-00001-of-00001.safetensors")) + shutil.copy(os.path.join(hfdir, "config.json"), fake) + print(f"fake TML checkpoint: {fake} ({len(inv)} tensors)") + for tag, xbits in [("pass", 0), ("i4", 4)]: + convert_dir(fake, f"{outbase}-{tag}", xbits) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--indir") + ap.add_argument("--outdir") + ap.add_argument("--xbits", type=int, default=4, choices=[0, 4, 8], + help="routed-expert bits: 4 (default), 8, or 0 = bf16 passthrough") + ap.add_argument("--watch", action="store_true", + help="poll --indir while the download is still running") + ap.add_argument("--delete-src", action="store_true") + ap.add_argument("--selftest", action="store_true") + ap.add_argument("--selftest-e2e", nargs=2, metavar=("HFDIR", "OUTBASE")) + a = ap.parse_args() + if a.selftest: + selftest(); return + if a.selftest_e2e: + selftest_e2e(*a.selftest_e2e); return + if not a.indir or not a.outdir: + ap.error("--indir and --outdir required") + os.makedirs(a.outdir, exist_ok=True) + lock = open(os.path.join(a.outdir, ".convert.lock"), "w") + import fcntl + try: + fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB) + except OSError: + sys.exit("ERROR: another converter is already using this output directory.") + convert_dir(a.indir, a.outdir, a.xbits, watch=a.watch, delete_src=a.delete_src) + + +if __name__ == "__main__": + main() From 92bdb875151a65115bcb9714d6cf4cb5c1c05783 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 15 Jul 2026 23:24:30 -0400 Subject: [PATCH 07/96] inkling: bf16-resident weights + AVX-512 BF16/VNNI kernels MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit f32 residents were fine for the tiny oracle but the real checkpoint needs ~172 GB that way — over sabre's 187 GB before the expert cache. Large matmul weights now stay in their on-disk dtype (Wt): raw bf16 for the real model (~86 GB resident), f32 for the oracle so validation stays bit-exact. Dispatch via matmul_w; embedding rows and lm_head included. Kernels for Zen 4: - matmul_h: bf16 dot via vdpbf16ps (activations rounded to bf16 per row, matching the HF bf16 reference numerics); shift-to-f32 scalar fallback on machines without AVX512-BF16 - matmul_q: Q8 per-32-block activation quantization + VNNI _mm256_dpbusd_epi32 int8 dot (maddubs fallback on plain AVX2), same IDOT family as glm.c; IDOT=0 keeps the byte-exact scalar path Oracle regressions all pass: f32 36/36+24/24, int4 container with VNNI and with IDOT=0 36/36+24/24, and a bf16-cast snapshot through the vdpbf16ps path also reproduces the oracle exactly. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 194 +++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 163 insertions(+), 31 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index d87d3ccf..87155d97 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -56,18 +56,23 @@ typedef struct { #define L_HD(c,i) ((c)->local[i] ? (c)->swa_hd : (c)->head_dim) #define L_EXT(c,i) ((c)->local[i] ? (c)->window : (c)->rel_extent) -/* ---------- resident per-layer weights (f32) ---------- */ +/* ---------- resident weights ---------- + * Large matmul weights keep their on-disk dtype in RAM: bf16 for the real + * 975B checkpoint (f32 residents would need ~172 GB, over sabre's 187), + * f32 for the tiny oracle (bit-exact validation). Exactly one pointer set. */ +typedef struct { float *f; uint16_t *h; } Wt; + typedef struct { float *in_ln, *post_ln; - float *q, *k, *v, *r, *o; /* projections */ + Wt q, k, v, r, o; /* projections */ float *qn, *kn; /* per-head rmsnorm [head_dim] */ float *relp; /* [d_rel, ext] bias bank */ float *k_cw, *v_cw, *a_cw, *m_cw; /* sconv weights, [C*K] depthwise */ /* dense layers */ - float *dg, *du, *dd, dgs; + Wt dg, du, dd; float dgs; /* MoE layers */ float *router, *rbias, rgs; /* [E+ns, D], [E], scalar */ - float *sh_g, *sh_u, *sh_d; /* shared experts [ns][I,D] etc. */ + Wt sh_g, sh_u, sh_d; /* shared experts [ns][I,D] etc. */ } Layer; /* ---------- routed-expert LRU cache ---------- */ @@ -83,7 +88,8 @@ typedef struct { shards S; int quant_bits; /* 0 = f32 experts (oracle mode) */ int xq; /* experts on disk are a colibri container (U8 + .qs) */ - float *embed, *embed_norm, *lm_head, *final_norm; + Wt embed, lm_head; + float *embed_norm, *final_norm; Layer *L; LCache *cache; uint64_t clock, hits, miss; @@ -117,8 +123,113 @@ static void matmul(float *y, const float *x, const float *W, int S, int I, int O } } -/* y[1,O] = x @ q^T with per-row int quantization (same container math as olmoe.c) */ +#if defined(__AVX512BF16__) && defined(__AVX512F__) +#include +#define HAVE_BF16_DOT 1 +#endif +#if defined(__AVX2__) +#include +#endif + +/* bf16-weight matmul: activations rounded to bf16 per row (matches the HF + * bf16 reference numerics), hardware vdpbf16ps dot where available, + * shift-to-f32 scalar otherwise. */ +static void matmul_h(float *y, const float *x, const uint16_t *W, int S, int I, int O) { +#ifdef HAVE_BF16_DOT + if (I % 32 == 0) { + uint16_t *xh = malloc((size_t)S * I * sizeof(uint16_t)); + for (int s = 0; s < S; s++) { + const float *xs = x + (int64_t)s * I; + uint16_t *xd = xh + (int64_t)s * I; + for (int i = 0; i < I; i += 32) { + __m512 a = _mm512_loadu_ps(xs + i), b = _mm512_loadu_ps(xs + i + 16); + _mm512_storeu_si512(xd + i, (__m512i)_mm512_cvtne2ps_pbh(b, a)); + } + } + #pragma omp parallel for schedule(static) + for (int o = 0; o < O; o++) { + const uint16_t *w = W + (int64_t)o * I; + for (int s = 0; s < S; s++) { + const uint16_t *xs = xh + (int64_t)s * I; + __m512 acc = _mm512_setzero_ps(); + for (int i = 0; i < I; i += 32) + acc = _mm512_dpbf16_ps(acc, (__m512bh)_mm512_loadu_si512(xs + i), + (__m512bh)_mm512_loadu_si512(w + i)); + y[(int64_t)s * O + o] = _mm512_reduce_add_ps(acc); + } + } + free(xh); + return; + } +#endif + #pragma omp parallel for schedule(static) + for (int o = 0; o < O; o++) { + const uint16_t *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++) { + union { uint32_t u; float f; } v = { (uint32_t)w[i] << 16 }; + acc += xs[i] * v.f; + } + y[(int64_t)s * O + o] = acc; + } + } +} + +/* dispatch on the weight's resident dtype */ +static void matmul_w(float *y, const float *x, Wt W, int S, int I, int O) { + if (W.f) matmul(y, x, W.f, S, I, O); + else matmul_h(y, x, W.h, S, I, O); +} + +/* y[1,O] = x @ q^T, int8 weights + per-row scale. Fast path: activations + * quantized Q8 per 32-block, VNNI (or maddubs) int8 dot — same family as + * glm.c's IDOT kernels; IDOT=0 falls back to the byte-exact scalar route. */ +#if defined(__AVX2__) +static inline __m256i i8dot_block(__m256i acc, __m256i a, __m256i b) { + __m256i ax = _mm256_sign_epi8(a, a); /* |a| as u8 */ + __m256i sy = _mm256_sign_epi8(b, a); /* b * sign(a) */ +#if defined(__AVX512VNNI__) && defined(__AVX512VL__) + return _mm256_dpbusd_epi32(acc, ax, sy); +#else + __m256i p = _mm256_maddubs_epi16(ax, sy); + return _mm256_add_epi32(acc, _mm256_madd_epi16(p, _mm256_set1_epi16(1))); +#endif +} +#endif static void matmul_q(float *y, const float *x, const int8_t *q, const float *scale, int I, int O) { +#if defined(__AVX2__) + static int idot = -1; + if (idot < 0) { const char *e = getenv("IDOT"); idot = !(e && *e == '0'); } + if (idot && I % 32 == 0 && I <= 8192) { + int nb = I / 32; + int8_t xi[8192]; float xs[256]; + for (int b = 0; b < nb; b++) { + const float *xb = x + b*32; + float am = 0.f; for (int i = 0; i < 32; 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 < 32; i++) xi[b*32+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++) { + __m256i vacc = i8dot_block(_mm256_setzero_si256(), + _mm256_loadu_si256((const __m256i*)(xi + b*32)), + _mm256_loadu_si256((const __m256i*)(w + b*32))); + __m128i lo = _mm256_castsi256_si128(vacc), hi = _mm256_extracti128_si256(vacc, 1); + __m128i s4 = _mm_add_epi32(lo, hi); + s4 = _mm_hadd_epi32(s4, s4); s4 = _mm_hadd_epi32(s4, s4); + acc += xs[b] * (float)_mm_cvtsi128_si32(s4); + } + 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; @@ -271,6 +382,25 @@ static float load_scalar(Model *m, const char *name, float dflt) { float v; st_read_f32(&m->S, name, &v, 0); return v; } +/* big matmul weights keep their on-disk dtype resident: BF16 raw (real + * checkpoint, halves RAM), anything else as f32 (tiny oracle: bit-exact) */ +static Wt load_w(Model *m, const char *name) { + Wt w = {0}; + st_tensor *t = st_find(&m->S, name); + if (!t) { fprintf(stderr, "missing %s\n", name); exit(1); } + if (t->dtype == 0) { w.h = malloc(t->nbytes); if (!w.h) { fprintf(stderr,"OOM %s\n",name); exit(1); } st_read_raw(&m->S, name, w.h, 0); } + else { w.f = falloc(t->numel); st_read_f32(&m->S, name, w.f, 0); } + return w; +} +static Wt wt_off(Wt w, int64_t off) { + Wt r = { w.f ? w.f + off : NULL, w.h ? w.h + off : NULL }; + return r; +} +static void wt_row_f32(Wt w, int64_t off, float *out, int n) { + if (w.f) memcpy(out, w.f + off, n * sizeof(float)); + else for (int i = 0; i < n; i++) { union { uint32_t u; float f; } v = { (uint32_t)w.h[off + i] << 16 }; out[i] = v.f; } +} + /* f32 slice of a (possibly bf16/f16) tensor: element offset + count. * Needed to stream one expert out of the fused [E,2I,D]/[E,D,I] tensors. */ static void read_f32_slice(shards *S, const char *name, float *out, int64_t off, int64_t cnt) { @@ -319,20 +449,21 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { Cfg *c = &m->c; int D = c->hidden, K = c->conv_k; double t0 = now_s(); - m->embed = load_t(m, "model.embed_tokens.weight"); + m->embed = load_w(m, "model.embed_tokens.weight"); m->embed_norm = st_has(&m->S,"model.embed_norm.weight") ? load_t(m,"model.embed_norm.weight") : NULL; m->final_norm = load_t(m, "model.norm.weight"); - m->lm_head = load_t(m, "lm_head.weight"); + m->lm_head = load_w(m, "lm_head.weight"); m->L = calloc(c->n_layers, sizeof(Layer)); char nm[320]; 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) + #define LD(field, suffix) snprintf(nm,sizeof(nm),"model.layers.%d." suffix,i); l->field = load_t(m,nm) + #define LDW(field, suffix) snprintf(nm,sizeof(nm),"model.layers.%d." suffix,i); l->field = load_w(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(r, "self_attn.r_proj.weight"); - LD(o, "self_attn.o_proj.weight"); + LDW(q, "self_attn.q_proj.weight"); LDW(k, "self_attn.k_proj.weight"); + LDW(v, "self_attn.v_proj.weight"); LDW(r, "self_attn.r_proj.weight"); + LDW(o, "self_attn.o_proj.weight"); LD(qn,"self_attn.q_norm.weight"); LD(kn,"self_attn.k_norm.weight"); LD(relp, "self_attn.rel_logits_proj.proj"); LD(k_cw, "self_attn.k_sconv.conv1d.weight"); @@ -340,17 +471,18 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { LD(a_cw, "attn_sconv.conv1d.weight"); LD(m_cw, "mlp_sconv.conv1d.weight"); if (!c->sparse[i]) { - LD(dg, "mlp.gate_proj.weight"); LD(du, "mlp.up_proj.weight"); LD(dd, "mlp.down_proj.weight"); + LDW(dg, "mlp.gate_proj.weight"); LDW(du, "mlp.up_proj.weight"); LDW(dd, "mlp.down_proj.weight"); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.global_scale",i); l->dgs = load_scalar(m,nm,1.f); } else { LD(router, "mlp.gate.weight"); LD(rbias, "mlp.gate.e_score_correction_bias"); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.gate.global_scale",i); l->rgs = load_scalar(m,nm,1.f); - LD(sh_g, "mlp.shared_experts.gate_proj"); - LD(sh_u, "mlp.shared_experts.up_proj"); - LD(sh_d, "mlp.shared_experts.down_proj"); + LDW(sh_g, "mlp.shared_experts.gate_proj"); + LDW(sh_u, "mlp.shared_experts.up_proj"); + LDW(sh_d, "mlp.shared_experts.down_proj"); } #undef LD + #undef LDW /* conv states: raw inputs of the previous K-1 steps, zero-init */ int kvdim = L_KV(c,i) * L_HD(c,i); for (int j = 0; j < 4; j++) { @@ -438,10 +570,10 @@ static void attention(Model *m, Layer *l, int li, float *x, int S, int pos0, flo float *k = falloc((int64_t)S*kvdim); float *vv = falloc((int64_t)S*kvdim); float *rr = falloc((int64_t)S*H*c->d_rel); - matmul(q, x, l->q, S, D, qdim); - matmul(k, x, l->k, S, D, kvdim); - matmul(vv, x, l->v, S, D, kvdim); - matmul(rr, x, l->r, S, D, H*c->d_rel); + matmul_w(q, x, l->q, S, D, qdim); + matmul_w(k, x, l->k, S, D, kvdim); + matmul_w(vv, x, l->v, S, D, kvdim); + matmul_w(rr, x, l->r, S, D, H*c->d_rel); /* short convs on K and V (sequence-wise, over the raw projections) */ sconv_apply(k, S, kvdim, l->k_cw, m->cs[0][li], c->conv_k); sconv_apply(vv, S, kvdim, l->v_cw, m->cs[1][li], c->conv_k); @@ -503,7 +635,7 @@ static void attention(Model *m, Layer *l, int li, float *x, int S, int pos0, flo } free(rl); free(sc); } - matmul(out, ctx, l->o, S, qdim, D); + matmul_w(out, ctx, l->o, S, qdim, D); free(q); free(k); free(vv); free(rr); free(ctx); } @@ -511,10 +643,10 @@ static void attention(Model *m, Layer *l, int li, float *x, int S, int pos0, flo static void dense_mlp(Model *m, Layer *l, float *x, int S, float *out) { Cfg *c = &m->c; int D = c->hidden, I = c->dense_inter; float *g = falloc((int64_t)S*I), *u = falloc((int64_t)S*I); - matmul(g, x, l->dg, S, D, I); - matmul(u, x, l->du, S, D, I); + matmul_w(g, x, l->dg, S, D, I); + matmul_w(u, x, l->du, S, D, I); for (int64_t i = 0; i < (int64_t)S*I; i++) g[i] = siluf(g[i]) * u[i]; - matmul(out, g, l->dd, S, I, D); + matmul_w(out, g, l->dd, S, I, D); for (int64_t i = 0; i < (int64_t)S*D; i++) out[i] *= l->dgs; free(g); free(u); } @@ -566,10 +698,10 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { } /* shared experts: gamma inside (before down_proj is linear, so applied at the end) */ for (int j = 0; j < ns; j++) { - matmul(g, xs, l->sh_g + (int64_t)j*I*D, 1, D, I); - matmul(u, xs, l->sh_u + (int64_t)j*I*D, 1, D, I); + matmul_w(g, xs, wt_off(l->sh_g, (int64_t)j*I*D), 1, D, I); + matmul_w(u, xs, wt_off(l->sh_u, (int64_t)j*I*D), 1, D, I); for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; - matmul(hh, g, l->sh_d + (int64_t)j*D*I, 1, I, D); + matmul_w(hh, g, wt_off(l->sh_d, (int64_t)j*D*I), 1, I, D); for (int d = 0; d < D; d++) os[d] += w[K+j] * hh[d]; } } @@ -583,8 +715,8 @@ static float *step(Model *m, const int *ids, int S, int pos0, int *tf_out) { Cfg *c = &m->c; int D = c->hidden; float *x = falloc((int64_t)S*D); for (int s = 0; s < S; s++) { - if (m->embed_norm) rmsnorm_row(x + (int64_t)s*D, m->embed + (int64_t)ids[s]*D, m->embed_norm, D, c->eps); - else memcpy(x + (int64_t)s*D, m->embed + (int64_t)ids[s]*D, D*sizeof(float)); + wt_row_f32(m->embed, (int64_t)ids[s]*D, x + (int64_t)s*D, D); + if (m->embed_norm) rmsnorm_row(x + (int64_t)s*D, x + (int64_t)s*D, m->embed_norm, D, c->eps); } float *nrm = falloc((int64_t)S*D), *tmp = falloc((int64_t)S*D); for (int i = 0; i < c->n_layers; i++) { @@ -606,14 +738,14 @@ static float *step(Model *m, const int *ids, int S, int pos0, int *tf_out) { for (int s = 0; s < S; s++) { rmsnorm_row(last, x + (int64_t)s*D, m->final_norm, D, c->eps); for (int d = 0; d < D; d++) last[d] /= c->mup; - matmul(logit, last, m->lm_head, 1, D, c->unpad_vocab); + matmul_w(logit, last, m->lm_head, 1, D, c->unpad_vocab); int best = 0; for (int i = 1; i < c->unpad_vocab; i++) if (logit[i] > logit[best]) best = i; tf_out[pos0 + s] = best; } } rmsnorm_row(last, x + (int64_t)(S-1)*D, m->final_norm, D, c->eps); for (int d = 0; d < D; d++) last[d] /= c->mup; - matmul(logit, last, m->lm_head, 1, D, c->unpad_vocab); + matmul_w(logit, last, m->lm_head, 1, D, c->unpad_vocab); free(x); free(nrm); free(tmp); free(last); return logit; } From 593bc3d0fad3ee3cd9eae7dd36735dfb6595911e Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 01:38:35 -0400 Subject: [PATCH 08/96] convert_inkling: thread the per-expert quantization (12 workers) Single-threaded numpy quantization paced the whole conversion at ~15 min per expert shard (~10+ hours for the checkpoint). Slice reads stay sequential for the HDD; the quantize work fans out on a thread pool (numpy ufuncs release the GIL on large arrays), in waves of 16 experts to bound peak RAM. Measured on the real checkpoint: 20 GB expert shard -> 5.5 GB int4 in 196 s, ~4.6x faster end to end. Co-Authored-By: Claude Fable 5 --- c/tools/convert_inkling_int4.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/c/tools/convert_inkling_int4.py b/c/tools/convert_inkling_int4.py index fb7985c2..cc476cff 100644 --- a/c/tools/convert_inkling_int4.py +++ b/c/tools/convert_inkling_int4.py @@ -133,8 +133,12 @@ def map_name(name): # ---------- per-shard conversion ---------- def quantize_expert_tensor(f, name, xbits, out, out_name): - """Chunked (per-expert) de-interleave/quantize of the fused 3D expert - tensors, so peak RAM stays at one expert slice, not one 19 GB tensor.""" + """Per-expert de-interleave/quantize of the fused 3D expert tensors. + Slice reads stay sequential (kind to the HDD); the numpy quantization — + the CPU bottleneck, ~95% of the checkpoint flows through here — fans out + on a thread pool (ufuncs release the GIL on large arrays). Waves of 16 + keep peak RAM at ~1.2 GB of pending slices instead of the whole tensor.""" + from concurrent.futures import ThreadPoolExecutor sl = f.get_slice(name) E, R, C = sl.get_shape() # w13: [E,2I,D] (interleaved), w2: [E,D,I] is_w13 = name.endswith("w13_weight") @@ -143,14 +147,20 @@ def quantize_expert_tensor(f, name, xbits, out, out_name): else: qs = np.empty((E * R, C), np.uint8) sc = np.empty(E * R, np.float32) - for e in range(E): - w = sl[e] # [R, C] bf16 + + def work(e, w): if is_w13: w = deinterleave(w, 0) # -> [gate rows; up rows] w = w.float().numpy() q, s = quant_int4_rows(w) if xbits == 4 else quant_int8_rows(w) qs[e * R:(e + 1) * R] = q sc[e * R:(e + 1) * R] = s + + with ThreadPoolExecutor(max_workers=12) as ex: + for base in range(0, E, 16): + futs = [ex.submit(work, e, sl[e]) for e in range(base, min(base + 16, E))] + for fu in futs: + fu.result() out[out_name] = torch.from_numpy(qs.reshape(-1)) out[out_name + ".qs"] = torch.from_numpy(sc) From de12586af4659a7b1ff6b966276b8cfcdfa38226 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 08:06:50 -0400 Subject: [PATCH 09/96] convert_inkling: completion check counts only model-* shards The checkpoint index also references mtp.safetensors (the separate MTP-head file we skip entirely), so the watch loop never saw itself as finished. Filter the completion set to the shards we convert. Co-Authored-By: Claude Fable 5 --- c/tools/convert_inkling_int4.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/c/tools/convert_inkling_int4.py b/c/tools/convert_inkling_int4.py index cc476cff..0960b8f5 100644 --- a/c/tools/convert_inkling_int4.py +++ b/c/tools/convert_inkling_int4.py @@ -250,12 +250,15 @@ def convert_dir(indir, outdir, xbits, watch=False, delete_src=False): flush=True) if delete_src: os.remove(sp) - # completion check: every shard named in the index is converted + # completion check: every model-* shard named in the index is + # converted. The index also references mtp.safetensors (the separate + # MTP-head file, skipped entirely) — only count files we convert. total = None if os.path.exists(index_path): wm = json.load(open(index_path))["weight_map"] - total = sorted(set(wm.values())) - if all(os.path.exists(os.path.join(outdir, shard_out_name(s))) for s in total): + total = sorted(s for s in set(wm.values()) + if re.match(r"model-\d+-of-\d+\.safetensors$", s)) + if total and all(os.path.exists(os.path.join(outdir, shard_out_name(s))) for s in total): break elif shards and not watch: break From 32b97a70df515b0baabe92489a2f1eec07c26af1 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 08:19:27 -0400 Subject: [PATCH 10/96] inkling: chunked pread for >2GB resident tensors Linux caps a single pread at ~2.147 GB; the bf16 embed and lm_head tensors are 2.47 GB each, so load_w died on a short read. Loop in 1 GB chunks. First real-checkpoint smoke test passes with this: coherent greedy completion from the full 975B model, RSS 90.5 GB. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index 87155d97..d56e0ac8 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -382,14 +382,31 @@ static float load_scalar(Model *m, const char *name, float dflt) { float v; st_read_f32(&m->S, name, &v, 0); return v; } +/* chunked pread: a single pread caps at ~2.1 GB on Linux, and the bf16 + * embed/lm_head tensors are 2.47 GB — loop in 1 GB slices */ +static void pread_all(int fd, void *buf, int64_t nb, int64_t off) { + char *p = buf; + while (nb > 0) { + int64_t chunk = nb < (1<<30) ? nb : (1<<30); + ssize_t got = pread(fd, p, (size_t)chunk, off); + if (got <= 0) { perror("pread chunk"); exit(1); } + p += got; off += got; nb -= got; + } +} + /* big matmul weights keep their on-disk dtype resident: BF16 raw (real * checkpoint, halves RAM), anything else as f32 (tiny oracle: bit-exact) */ static Wt load_w(Model *m, const char *name) { Wt w = {0}; st_tensor *t = st_find(&m->S, name); if (!t) { fprintf(stderr, "missing %s\n", name); exit(1); } - if (t->dtype == 0) { w.h = malloc(t->nbytes); if (!w.h) { fprintf(stderr,"OOM %s\n",name); exit(1); } st_read_raw(&m->S, name, w.h, 0); } - else { w.f = falloc(t->numel); st_read_f32(&m->S, name, w.f, 0); } + if (t->dtype == 0) { + w.h = malloc(t->nbytes); if (!w.h) { fprintf(stderr,"OOM %s\n",name); exit(1); } + pread_all(t->fd, w.h, t->nbytes, t->off); + } else { + w.f = falloc(t->numel); + st_read_f32(&m->S, name, w.f, 0); + } return w; } static Wt wt_off(Wt w, int64_t off) { From 1ee59cc8af36b9eb47020afcd28e94bae285b74f Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 10:01:28 -0400 Subject: [PATCH 11/96] inkling perf: packed-int4 cache, parallel expert fills, usage pinning, auto cap MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Stage-B of the expert path, all glm.c-playbook: - cache slots keep the container rows PACKED (int4 stays 4-bit in RAM: ~28 MB/expert instead of ~57 unpacked, double the experts per GB); new matmul_q4 unpacks nibbles in-register (AVX2 interleave + VNNI dot), numerically identical to the unpack+int8 path - moe() is now three passes: route everything, fill ALL missing experts in one parallel burst (prefill batches the whole sequence's misses -> NVMe finally sees queue depth), then compute - usage-history pinning: selection counts persist to SNAP/.coli_usage (glm convention, IKU1 header); next run pins the top PIN_N per layer (default cap/4) as non-evictable slots, filled in parallel at startup - cap 0 = auto-sized from MemAvailable after residents load; generate mode defaults to auto Oracle regressions all green: f32, int4 container (VNNI + IDOT=0), bf16 residents — 36/36 teacher-forced + 24/24 greedy each. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 356 +++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 298 insertions(+), 58 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index d56e0ac8..53cf4675 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -75,11 +75,17 @@ typedef struct { Wt sh_g, sh_u, sh_d; /* shared experts [ns][I,D] etc. */ } Layer; -/* ---------- routed-expert LRU cache ---------- */ +/* ---------- routed-expert cache: LRU + optional pinned set ---------- + * Container snapshots keep the expert rows PACKED in RAM (int4 stays 4-bit: + * ~28 MB/expert instead of ~57 unpacked, so the same budget caches twice the + * experts); the matmul kernels unpack nibbles in-register. */ typedef struct { int eid; uint64_t used; - int8_t *q13, *q2; float *s13, *s2; /* bits>0: int-quantized rows */ - float *f13, *f2; /* bits==0: raw f32 */ + int pinned; /* never evicted (usage-history pin) */ + int filled; /* 0 while queued for a parallel fill */ + uint8_t *p13, *p2; float *s13, *s2; /* container: packed rows + row scales */ + int8_t *q13, *q2; /* bits>0: runtime-quantized int8 */ + float *f13, *f2; /* bits==0: raw f32 (oracle) */ } Slot; typedef struct { Slot *slots; int n, cap; } LCache; @@ -92,6 +98,9 @@ typedef struct { float *embed_norm, *final_norm; Layer *L; LCache *cache; + int64_t rb13, rb2; /* container row-bytes (0 = not container) */ + uint32_t **eusage; /* per-layer expert selection counts */ + int npin; /* pinned experts per sparse layer */ uint64_t clock, hits, miss; float **K, **V; int kv_len, max_t; /* per-layer [kv][max_t][hd] */ float **cs[4]; /* conv states, [n_layers][C*(K-1)] */ @@ -239,6 +248,62 @@ static void matmul_q(float *y, const float *x, const int8_t *q, const float *sca } } +/* y[1,O] = x @ W^T with W kept PACKED int4 (low nibble = even column, +8 + * offset, per-row scale — the on-disk container layout, cached as-is). + * Nibbles unpack in-register: same numeric result as unpack-to-int8 + + * matmul_q, half the cache footprint. IDOT=0 keeps the byte-exact scalar. */ +static void matmul_q4(float *y, const float *x, const uint8_t *p, const float *scale, int I, int O) { +#if defined(__AVX2__) + static int idot = -1; + if (idot < 0) { const char *e = getenv("IDOT"); idot = !(e && *e == '0'); } + if (idot && I % 32 == 0 && I <= 8192) { + int nb = I / 32; + int8_t xi[8192]; float xs[256]; + for (int b = 0; b < nb; b++) { + const float *xb = x + b*32; + float am = 0.f; for (int i = 0; i < 32; 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 < 32; i++) xi[b*32+i] = (int8_t)lrintf(xb[i]*inv); + } + const __m128i m4 = _mm_set1_epi8(0x0F); + const __m256i b8 = _mm256_set1_epi8(8); + #pragma omp parallel for schedule(static) + for (int o = 0; o < O; o++) { + const uint8_t *w = p + (int64_t)o * (I/2); + float acc = 0.f; + for (int b = 0; b < nb; b++) { + __m128i by = _mm_loadu_si128((const __m128i*)(w + b*16)); /* 16 B = 32 nibbles */ + __m128i lo = _mm_and_si128(by, m4); /* even columns */ + __m128i hi = _mm_and_si128(_mm_srli_epi16(by, 4), m4); /* odd columns */ + __m256i nib = _mm256_set_m128i(_mm_unpackhi_epi8(lo, hi), /* cols 16..31 */ + _mm_unpacklo_epi8(lo, hi)); /* cols 0..15 */ + nib = _mm256_sub_epi8(nib, b8); + __m256i vacc = i8dot_block(_mm256_setzero_si256(), + _mm256_loadu_si256((const __m256i*)(xi + b*32)), nib); + __m128i l = _mm256_castsi256_si128(vacc), h = _mm256_extracti128_si256(vacc, 1); + __m128i s4 = _mm_add_epi32(l, h); + s4 = _mm_hadd_epi32(s4, s4); s4 = _mm_hadd_epi32(s4, s4); + acc += xs[b] * (float)_mm_cvtsi128_si32(s4); + } + y[o] = acc * scale[o]; + } + return; + } +#endif + #pragma omp parallel for schedule(static) + for (int o = 0; o < O; o++) { + const uint8_t *w = p + (int64_t)o * (I/2); + float acc = 0.f; + for (int i = 0; i < I; i += 2) { + uint8_t byte = w[i/2]; + acc += x[i] * (float)((int)(byte & 0xF) - 8); + acc += x[i+1] * (float)((int)(byte >> 4) - 8); + } + y[o] = acc * scale[o]; + } +} + static void quantize_rows(const float *w, int8_t *q, float *scale, int O, int I, int bits) { int qmax = (1 << (bits - 1)) - 1; #pragma omp parallel for schedule(static) @@ -458,6 +523,8 @@ static void unpack_rows(const uint8_t *raw, int8_t *q, int64_t rows, int64_t col } } +static double mem_avail_bytes(void); + static void model_init(Model *m, const char *snap, int cap, int bits) { memset(m, 0, sizeof(*m)); m->quant_bits = bits; @@ -508,73 +575,193 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { m->cs[j][i] = calloc((int64_t)C * (K-1), sizeof(float)); } } - 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)); } - /* container detection: converted snapshots store experts as U8 + .qs */ + /* container detection: converted snapshots store experts as U8 + .qs. + * rb13/rb2 = bytes per packed row (D/2|D and I/2|I for int4|int8) */ + int64_t I = c->moe_inter, E = c->n_experts; for (int i = 0; i < c->n_layers; i++) if (c->sparse[i]) { snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",i); st_tensor *t = st_find(&m->S, nm); - if (t) m->xq = (t->dtype == 3); + if (t && t->dtype == 3) { + m->xq = 1; + m->rb13 = t->nbytes / (E * 2*I); + snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",i); + st_tensor *t2 = st_find(&m->S, nm); + m->rb2 = t2->nbytes / (E * (int64_t)D); + if (m->rb13 != D && m->rb13*2 != D) { fprintf(stderr,"unsupported container row size %lld\n",(long long)m->rb13); exit(1); } + } break; } + int nsp = 0; for (int i = 0; i < c->n_layers; i++) nsp += c->sparse[i]; + int64_t slotb = m->xq ? m->rb13*2*I + m->rb2*D + (2*I+D)*4 + : m->quant_bits ? 3*I*D + (2*I+D)*4 : 3*I*D*4; + if (cap <= 0) { /* auto: fit the LRU in available RAM, 20% + 4 GB headroom */ + double avail = mem_avail_bytes(); + cap = avail > 0 ? (int)((avail*0.80 - 4e9) / ((double)slotb * (nsp ? nsp : 1))) : 16; + if (cap < 4) cap = 4; + if (cap > c->n_experts) cap = c->n_experts; + fprintf(stderr, "[cap auto] %d experts/layer (%.1f GB cache budget)\n", + cap, (double)cap*slotb*nsp/1e9); + } + 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)); } + /* usage counters; seeded from a previous run's history when present */ + m->eusage = calloc(c->n_layers, sizeof(uint32_t*)); + for (int i = 0; i < c->n_layers; i++) if (c->sparse[i]) m->eusage[i] = calloc(E, 4); m->dense_load_s = now_s() - t0; } -/* ---------- routed-expert fetch: slice of fused tensors, LRU-cached ---------- */ -static void expert_get(Model *m, int layer, int eid, Slot **out) { +static double mem_avail_bytes(void) { +#if defined(__linux__) + FILE *f = fopen("/proc/meminfo", "r"); + if (!f) return 0; + char ln[256]; double kb = 0; + while (fgets(ln, sizeof(ln), f)) if (sscanf(ln, "MemAvailable: %lf", &kb) == 1) break; + fclose(f); + return kb * 1024.0; +#else + return 0; +#endif +} + +/* ---------- routed-expert slots: serial bookkeeping, parallel fills ---------- */ +static Slot *slot_find(Model *m, int layer, int eid) { LCache *lc = &m->cache[layer]; 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; + lc->slots[i].used = ++m->clock; + return &lc->slots[i]; } - m->miss++; - Cfg *c = &m->c; - int64_t D = c->hidden, I = c->moe_inter; - int64_t n13 = 2*I*D, n2 = D*I; + return NULL; +} + +/* allocate a slot (or evict the LRU non-pinned one); serial callers only */ +static Slot *slot_acquire(Model *m, int layer, int eid) { + LCache *lc = &m->cache[layer]; Cfg *c = &m->c; + int64_t D = c->hidden, I = c->moe_inter, n13 = 2*I*D, n2 = D*I; Slot *s; if (lc->n < lc->cap) { s = &lc->slots[lc->n++]; - if (m->quant_bits || m->xq) { - s->q13 = malloc(n13); s->q2 = malloc(n2); - s->s13 = falloc(2*I); s->s2 = falloc(D); - } else { s->f13 = falloc(n13); s->f2 = falloc(n2); } - } 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]; } + if (m->xq) { s->p13 = malloc((size_t)(m->rb13*2*I)); s->p2 = malloc((size_t)(m->rb2*D)); + s->s13 = falloc(2*I); s->s2 = falloc(D); + if (!s->p13 || !s->p2) { fprintf(stderr,"OOM expert slot\n"); exit(1); } } + else if (m->quant_bits) { s->q13 = malloc(n13); s->q2 = malloc(n2); + s->s13 = falloc(2*I); s->s2 = falloc(D); + if (!s->q13 || !s->q2) { fprintf(stderr,"OOM expert slot\n"); exit(1); } } + else { s->f13 = falloc(n13); s->f2 = falloc(n2); } + } else { + int lru = -1; + for (int i = 0; i < lc->n; i++) + if (!lc->slots[i].pinned && (lru < 0 || lc->slots[i].used < lc->slots[lru].used)) lru = i; + if (lru < 0) { fprintf(stderr, "layer %d: cache cap %d entirely pinned\n", layer, lc->cap); exit(1); } + s = &lc->slots[lru]; + } + s->eid = eid; s->used = ++m->clock; s->filled = 0; s->pinned = 0; + return s; +} + +/* pure I/O (+ optional requant): safe to run in parallel across slots */ +static void slot_fill(Model *m, int layer, Slot *s) { + Cfg *c = &m->c; + int64_t D = c->hidden, I = c->moe_inter, n13 = 2*I*D, n2 = D*I; + int64_t eid = s->eid; char nm[320], qs[340]; if (m->xq) { - /* colibri container: packed U8 rows + f32 row scales, raw read, no requant */ snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); - st_tensor *t = st_find(&m->S, nm); - int64_t rowb = t->nbytes / ((int64_t)c->n_experts * 2*I); /* D = int8, D/2 = int4 */ - uint8_t *raw = malloc((size_t)(2*I*rowb)); - read_u8_slice(&m->S, nm, raw, (int64_t)eid*2*I*rowb, 2*I*rowb); - unpack_rows(raw, s->q13, 2*I, D, rowb); + read_u8_slice(&m->S, nm, s->p13, eid*2*I*m->rb13, 2*I*m->rb13); snprintf(qs,sizeof(qs),"%s.qs",nm); - read_f32_slice(&m->S, qs, s->s13, (int64_t)eid*2*I, 2*I); + read_f32_slice(&m->S, qs, s->s13, eid*2*I, 2*I); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); - t = st_find(&m->S, nm); - rowb = t->nbytes / ((int64_t)c->n_experts * D); - raw = realloc(raw, (size_t)(D*rowb)); - read_u8_slice(&m->S, nm, raw, (int64_t)eid*D*rowb, D*rowb); - unpack_rows(raw, s->q2, D, I, rowb); + read_u8_slice(&m->S, nm, s->p2, eid*D*m->rb2, D*m->rb2); snprintf(qs,sizeof(qs),"%s.qs",nm); - read_f32_slice(&m->S, qs, s->s2, (int64_t)eid*D, D); - free(raw); + read_f32_slice(&m->S, qs, s->s2, eid*D, D); } else if (m->quant_bits) { float *tmp = falloc(n13 > n2 ? n13 : n2); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); - read_f32_slice(&m->S, nm, tmp, (int64_t)eid*n13, n13); + read_f32_slice(&m->S, nm, tmp, eid*n13, n13); quantize_rows(tmp, s->q13, s->s13, 2*I, D, m->quant_bits); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); - read_f32_slice(&m->S, nm, tmp, (int64_t)eid*n2, n2); + read_f32_slice(&m->S, nm, tmp, eid*n2, n2); quantize_rows(tmp, s->q2, s->s2, D, I, m->quant_bits); free(tmp); } else { snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.gate_up_proj",layer); - read_f32_slice(&m->S, nm, s->f13, (int64_t)eid*n13, n13); + read_f32_slice(&m->S, nm, s->f13, eid*n13, n13); snprintf(nm,sizeof(nm),"model.layers.%d.mlp.experts.down_proj",layer); - read_f32_slice(&m->S, nm, s->f2, (int64_t)eid*n2, n2); + read_f32_slice(&m->S, nm, s->f2, eid*n2, n2); } - s->eid = eid; s->used = ++m->clock; - *out = s; + s->filled = 1; +} + +/* pin the top-N experts per sparse layer from a usage-history file (colibri + * .coli_usage convention: one uint32 count per expert per layer). Pins are + * regular cache slots flagged non-evictable, filled in parallel at startup. */ +static void pins_load(Model *m, const char *snap) { + Cfg *c = &m->c; int E = c->n_experts; + char up[2048]; + const char *env = getenv("PIN"); + if (env) snprintf(up, sizeof(up), "%s", env); + else snprintf(up, sizeof(up), "%s/.coli_usage", snap); + FILE *f = fopen(up, "rb"); + if (!f) return; + uint32_t hdr[3]; + if (fread(hdr, 4, 3, f) != 3 || hdr[0] != 0x31554B49u || + (int)hdr[1] != c->n_layers || (int)hdr[2] != E) { + fprintf(stderr, "[pin] %s: not an inkling usage file, ignoring\n", up); + fclose(f); return; + } + int cap = m->cache[0].cap; + m->npin = getenv("PIN_N") ? atoi(getenv("PIN_N")) : cap/4; + if (m->npin > cap - 8) m->npin = cap - 8; + if (m->npin < 0) m->npin = 0; + uint32_t *tmp = malloc((size_t)E * 4); + Slot **ps = malloc((size_t)c->n_layers * m->npin * sizeof(Slot*)); + int *pl = malloc((size_t)c->n_layers * m->npin * sizeof(int)); + int np = 0; + for (int i = 0; i < c->n_layers; i++) { + if (fread(tmp, 4, E, f) != (size_t)E) break; + if (!c->sparse[i] || !m->npin) continue; + memcpy(m->eusage[i], tmp, (size_t)E * 4); /* seed the ranking */ + for (int r = 0; r < m->npin; r++) { /* top-N selection */ + int best = -1; uint32_t bv = 0; + for (int e = 0; e < E; e++) { + int taken = 0; + for (int z = 0; z < r; z++) if (ps[np-r+z]->eid == e) { taken = 1; break; } + if (!taken && tmp[e] >= bv && tmp[e] > 0) { bv = tmp[e]; best = e; } + } + if (best < 0) break; + Slot *s = slot_acquire(m, i, best); + s->pinned = 1; + ps[np] = s; pl[np] = i; np++; + } + } + fclose(f); + if (np) { + double t0 = now_s(); + #pragma omp parallel for schedule(dynamic,1) + for (int j = 0; j < np; j++) slot_fill(m, pl[j], ps[j]); + fprintf(stderr, "[pin] %d experts pinned (%d/layer) from %s in %.1fs\n", + np, m->npin, up, now_s()-t0); + } + free(tmp); free(ps); free(pl); +} + +/* usage snapshot: rewritten after every generation run (same contract as + * glm's .coli_usage — copy it aside if you need a stable ranking) */ +static void usage_save(Model *m, const char *snap) { + Cfg *c = &m->c; int E = c->n_experts; + char up[2048], tp[2060]; + const char *env = getenv("PIN"); + if (env) snprintf(up, sizeof(up), "%s", env); + else snprintf(up, sizeof(up), "%s/.coli_usage", snap); + snprintf(tp, sizeof(tp), "%s.tmp", up); + FILE *f = fopen(tp, "wb"); + if (!f) return; + uint32_t hdr[3] = { 0x31554B49u, (uint32_t)c->n_layers, (uint32_t)E }; + fwrite(hdr, 4, 3, f); + uint32_t *zero = calloc(E, 4); + for (int i = 0; i < c->n_layers; i++) + fwrite(m->eusage[i] ? m->eusage[i] : zero, 4, E, f); + free(zero); fclose(f); + rename(tp, up); } /* ---------- attention (GQA + sliding/global + relative bias + K/V sconv) ---------- */ @@ -668,7 +855,11 @@ static void dense_mlp(Model *m, Layer *l, float *x, int S, float *out) { free(g); free(u); } -/* ---------- MoE: sigmoid router + bias top-k, joint routed+shared weights ---------- */ +/* ---------- MoE: sigmoid router + bias top-k, joint routed+shared weights ---------- + * Three passes per layer call: (1) route every position and acquire slots, + * (2) fill ALL missing experts in one parallel burst (the NVMe wants queue + * depth — during prefill this batches the whole sequence's misses), then + * (3) compute. */ 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->moe_inter, ns = c->n_shared; @@ -676,41 +867,82 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { float *logits = falloc((int64_t)S*ET); matmul(logits, x, l->router, S, D, ET); memset(out, 0, (int64_t)S*D*sizeof(float)); - float *g = falloc(I), *u = falloc(I), *hh = falloc(D); + int *idx = malloc((size_t)S*K*sizeof(int)); + float *wgt = malloc((size_t)S*(K+ns)*sizeof(float)); + Slot **use = malloc((size_t)S*K*sizeof(Slot*)); + Slot **fill = malloc((size_t)S*K*sizeof(Slot*)); + int *fl = malloc((size_t)S*K*sizeof(int)); + int nfill = 0; + /* pass 1: routing + slot bookkeeping (serial) */ for (int s = 0; s < S; s++) { float *lg = logits + (int64_t)s*ET; + int *si = idx + (int64_t)s*K; /* selection: sigmoid(routed) + correction bias, top-K */ - int idx[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;} + int taken = 0; for (int j = 0; j < kk; j++) if (si[j]==e){taken=1;break;} float ch = sigmoidf(lg[e]) + l->rbias[e]; if (!taken && ch > bv) { bv = ch; best = e; } } - idx[kk] = best; + si[kk] = best; } /* combine weights: sigmoids of the raw logits of (topK routed + shared), * normalized to sum 1 over all K+ns, x route_scale x gate.global_scale */ - float w[80]; float sum = 0.f; - for (int kk = 0; kk < K; kk++) { w[kk] = sigmoidf(lg[idx[kk]]); sum += w[kk]; } - for (int j = 0; j < ns; j++) { w[K+j] = sigmoidf(lg[E+j]); sum += w[K+j]; } + float *w = wgt + (int64_t)s*(K+ns); float sum = 0.f; + for (int kk = 0; kk < K; kk++) { w[kk] = sigmoidf(lg[si[kk]]); sum += w[kk]; } + for (int j = 0; j < ns; j++) { w[K+j] = sigmoidf(lg[E+j]); sum += w[K+j]; } for (int kk = 0; kk < K+ns; kk++) w[kk] *= c->route_scale * l->rgs / sum; + for (int kk = 0; kk < K; kk++) { + int eid = si[kk]; + if (m->eusage[layer]) m->eusage[layer][eid]++; + Slot *e = slot_find(m, layer, eid); + if (e) m->hits++; + else { + m->miss++; + e = slot_acquire(m, layer, eid); + fill[nfill] = e; fl[nfill] = layer; nfill++; + } + use[(int64_t)s*K + kk] = e; + } + } + /* pass 2: one parallel burst for every miss in this layer call */ + if (nfill) { + #pragma omp parallel for schedule(dynamic,1) + for (int j = 0; j < nfill; j++) slot_fill(m, fl[j], fill[j]); + } + /* pass 3: compute */ + float *g = falloc(I), *u = falloc(I), *hh = falloc(D); + int q4 = m->xq && m->rb13*2 == D; /* packed int4 vs int8 container */ + for (int s = 0; s < S; s++) { const float *xs = x + (int64_t)s*D; float *os = out + (int64_t)s*D; + float *w = wgt + (int64_t)s*(K+ns); for (int kk = 0; kk < K; kk++) { - Slot *e; expert_get(m, layer, idx[kk], &e); - int qm = m->quant_bits || m->xq; - if (qm) { - matmul_q(g, xs, e->q13, e->s13, D, I); /* gate rows */ - matmul_q(u, xs, e->q13 + (int64_t)I*D, e->s13 + I, D, I); /* up rows */ + Slot *e = use[(int64_t)s*K + kk]; + if (m->xq) { + if (q4) { + matmul_q4(g, xs, e->p13, e->s13, D, I); /* gate rows */ + matmul_q4(u, xs, e->p13 + (int64_t)I*m->rb13, e->s13 + I, D, I); /* up rows */ + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + matmul_q4(hh, g, e->p2, e->s2, I, D); + } else { + matmul_q(g, xs, (int8_t*)e->p13, e->s13, D, I); + matmul_q(u, xs, (int8_t*)e->p13 + (int64_t)I*D, e->s13 + I, D, I); + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + matmul_q(hh, g, (int8_t*)e->p2, e->s2, I, D); + } + } else if (m->quant_bits) { + matmul_q(g, xs, e->q13, e->s13, D, I); + matmul_q(u, xs, e->q13 + (int64_t)I*D, e->s13 + I, D, I); + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + matmul_q(hh, g, e->q2, e->s2, I, D); } else { matmul(g, xs, e->f13, 1, D, I); matmul(u, xs, e->f13 + (int64_t)I*D, 1, D, I); + for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; + matmul(hh, g, e->f2, 1, I, D); } - for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; - if (qm) matmul_q(hh, g, e->q2, e->s2, I, D); - else matmul(hh, g, e->f2, 1, I, D); for (int d = 0; d < D; d++) os[d] += w[kk] * hh[d]; } /* shared experts: gamma inside (before down_proj is linear, so applied at the end) */ @@ -722,7 +954,8 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { for (int d = 0; d < D; d++) os[d] += w[K+j] * hh[d]; } } - free(logits); free(g); free(u); free(hh); + free(logits); free(idx); free(wgt); free(use); free(fill); free(fl); + free(g); free(u); free(hh); } /* ---------- one forward pass over S new tokens ---------- @@ -851,7 +1084,7 @@ int main(int argc, char **argv) { if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } /* flags: -p "prompt" [-n N] -> generate mode; positional: [cap] [bits] [ref.json] */ const char *prompt = NULL, *refpath = "ref_inkling.json"; - int cap = 16, bits = 0, n_new = 256, npos = 0; + int cap = -1, bits = 0, n_new = 256, npos = 0; for (int i = 1; i < argc; i++) { if (!strcmp(argv[i], "-p") && i+1 < argc) prompt = argv[++i]; else if (!strcmp(argv[i], "-n") && i+1 < argc) n_new = atoi(argv[++i]); @@ -859,15 +1092,22 @@ int main(int argc, char **argv) { else if (npos == 1) { bits = atoi(argv[i]); npos++; } else refpath = argv[i]; } + if (cap < 0) cap = prompt ? 0 : 16; /* generate mode defaults to RAM-sized auto cap */ if (bits && (bits < 2 || bits > 8)) { fprintf(stderr, "quant_bits must be 0 (f32) or 2..8\n"); return 1; } if (prompt) { Model m; model_init(&m, snap, cap, bits); printf("== Inkling C engine, %d layers, experts @ %s, cache %d/layer ==\n", - m.c.n_layers, m.xq ? "container" : bits ? "int" : "f32", cap); + m.c.n_layers, m.xq ? "container" : bits ? "int" : "f32", m.cache[0].cap); + pins_load(&m, snap); char tkp[2048]; snprintf(tkp, sizeof(tkp), "%s/tokenizer.json", snap); Tok T; tok_load(&T, tkp); generate_stream(&m, &T, prompt, n_new); + usage_save(&m, snap); + double tot = m.hits + m.miss; + printf("[cache] hit %.1f%% (%llu hit / %llu load) | usage history saved\n", + tot ? 100.0*m.hits/tot : 0.0, + (unsigned long long)m.hits, (unsigned long long)m.miss); return 0; } From 524f2a44763694549f760a24f474e418d26d8c46 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 10:39:45 -0400 Subject: [PATCH 12/96] inkling: fuse gate+up matmuls, OMP hot-thread re-exec (neutral on decode) Gate+up now run as one matmul over the fused 2I rows (half the parallel regions per expert, one buffer), plus glm.c's OMP_WAIT_POLICY re-exec. Honest result: decode unchanged at 0.25 tok/s, prefill 21.7->21.1s. The measurement that matters: decode is RAM-bandwidth-bound on the ~35 GB of bf16 residents read per token (~1.2s/token floor on dual channel DDR5), not OMP-overhead-bound like glm's int4 residents were. Keeping the changes for the structure; the bandwidth wall is the GPU tier's job. All oracle modes still token-exact. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 35 ++++++++++++++++++++++++----------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index 53cf4675..85fda0ef 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -911,8 +911,9 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { #pragma omp parallel for schedule(dynamic,1) for (int j = 0; j < nfill; j++) slot_fill(m, fl[j], fill[j]); } - /* pass 3: compute */ - float *g = falloc(I), *u = falloc(I), *hh = falloc(D); + /* pass 3: compute. gate+up run as ONE matmul over the fused 2I rows — + * halves the number of (expensive to open) parallel regions per expert */ + float *g = falloc(2*I), *u = g + I, *hh = falloc(D); int q4 = m->xq && m->rb13*2 == D; /* packed int4 vs int8 container */ for (int s = 0; s < S; s++) { const float *xs = x + (int64_t)s*D; @@ -922,24 +923,20 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { Slot *e = use[(int64_t)s*K + kk]; if (m->xq) { if (q4) { - matmul_q4(g, xs, e->p13, e->s13, D, I); /* gate rows */ - matmul_q4(u, xs, e->p13 + (int64_t)I*m->rb13, e->s13 + I, D, I); /* up rows */ + matmul_q4(g, xs, e->p13, e->s13, D, 2*I); /* gate rows then up rows */ for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; matmul_q4(hh, g, e->p2, e->s2, I, D); } else { - matmul_q(g, xs, (int8_t*)e->p13, e->s13, D, I); - matmul_q(u, xs, (int8_t*)e->p13 + (int64_t)I*D, e->s13 + I, D, I); + matmul_q(g, xs, (int8_t*)e->p13, e->s13, D, 2*I); for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; matmul_q(hh, g, (int8_t*)e->p2, e->s2, I, D); } } else if (m->quant_bits) { - matmul_q(g, xs, e->q13, e->s13, D, I); - matmul_q(u, xs, e->q13 + (int64_t)I*D, e->s13 + I, D, I); + matmul_q(g, xs, e->q13, e->s13, D, 2*I); for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; matmul_q(hh, g, e->q2, e->s2, I, D); } else { - matmul(g, xs, e->f13, 1, D, I); - matmul(u, xs, e->f13 + (int64_t)I*D, 1, D, I); + matmul(g, xs, e->f13, 1, D, 2*I); for (int i = 0; i < I; i++) g[i] = siluf(g[i]) * u[i]; matmul(hh, g, e->f2, 1, I, D); } @@ -955,7 +952,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { } } free(logits); free(idx); free(wgt); free(use); free(fill); free(fl); - free(g); free(u); free(hh); + free(g); free(hh); /* u aliases g+I */ } /* ---------- one forward pass over S new tokens ---------- @@ -1080,6 +1077,22 @@ static int *read_int_array(jval *o, const char *key, int *n_out) { } int main(int argc, char **argv) { + /* OpenMP hot-thread tuning, same trick (and rationale) as glm.c: the + * per-expert matmul regions are tiny and back-to-back; the default passive + * wait policy parks the team between regions and re-wake latency dominates. + * libgomp reads OMP_/GOMP_ vars before main(), so seed them and re-exec + * once (COLI_OMP_TUNED guards the exec; COLI_NO_OMP_TUNE=1 disables). */ + if (!getenv("COLI_OMP_TUNED") && !getenv("COLI_NO_OMP_TUNE")) { + setenv("OMP_WAIT_POLICY","active",0); + setenv("GOMP_SPINCOUNT","200000",0); + setenv("OMP_PROC_BIND","close",0); + setenv("OMP_DYNAMIC","FALSE",0); + setenv("COLI_OMP_TUNED","1",1); +#ifdef __linux__ + execv("/proc/self/exe", argv); + perror("[OMP] execv self-reexec failed, running untuned"); +#endif + } const char *snap = getenv("SNAP"); if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } /* flags: -p "prompt" [-n N] -> generate mode; positional: [cap] [bits] [ref.json] */ From a34edb24eb2db5b1af30fa1895338def21657b3b Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 10:57:27 -0400 Subject: [PATCH 13/96] =?UTF-8?q?inkling:=20CUDA=20tier=20=E2=80=94=20bf16?= =?UTF-8?q?=20residents=20in=20VRAM=20(A6000),=20+28%=20decode,=20best=20p?= =?UTF-8?q?refill?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New minimal backend (backend_cuda_ink.cu, CUDA=1 + existing flags): bf16 resident tensors upload to VRAM at load (~36 GB on the 975B; embed stays host for row lookup) and matmul_w dispatches to a one-warp-per-row bf16 kernel with the same numeric contract as the CPU vdpbf16ps path (both operands bf16-rounded, f32 accumulate). Freed RAM goes straight to the expert cache: cap auto 64->79/layer, hit 81.5->83.6%, RSS 116->82 GB. Two overheads found the hard way (bench4 regressed before these): - pinned host staging for activations — pageable cudaMemcpyAsync silently degrades to synchronous bounce copies - the OMP hot-thread re-exec is now #ifndef COLI_CUDA, the same exception glm.c makes: a spinning 24-thread team starves the CUDA driver during every stream sync 975B decode 0.25 -> 0.32 tok/s, prefill 21.1 -> 18.4 s. GPU path reproduces the tiny oracle exactly (36/36 + 24/24); CPU-only build unchanged and still oracle-exact in all modes. Co-Authored-By: Claude Fable 5 --- c/Makefile | 9 ++++- c/backend_cuda_ink.cu | 87 +++++++++++++++++++++++++++++++++++++++++++ c/backend_cuda_ink.h | 23 ++++++++++++ c/inkling.c | 59 ++++++++++++++++++++++++----- 4 files changed, 166 insertions(+), 12 deletions(-) create mode 100644 c/backend_cuda_ink.cu create mode 100644 c/backend_cuda_ink.h diff --git a/c/Makefile b/c/Makefile index fbb68a1d..c7175681 100644 --- a/c/Makefile +++ b/c/Makefile @@ -227,6 +227,7 @@ endif CFLAGS += -DCOLI_CUDA LDFLAGS += -L$(CUDA_HOME)/lib64 -Wl,-rpath,$(CUDA_HOME)/lib64 -lcudart -lstdc++ CUDA_OBJ = backend_cuda.o +INK_CUDA_OBJ = backend_cuda_ink.o endif ifeq ($(HIP),1) @@ -300,6 +301,10 @@ cuda-dll: backend_cuda.cu backend_cuda.h -L"$(CUDA_HOME)/lib/x64" -lcudart \ backend_cuda.cu -o coli_cuda.dll +backend_cuda_ink.o: backend_cuda_ink.cu backend_cuda_ink.h .build-config + @command -v "$(NVCC)" >/dev/null 2>&1 || { echo "nvcc not found: set CUDA_HOME or NVCC" >&2; exit 1; } + "$(NVCC)" $(NVCCFLAGS) -c backend_cuda_ink.cu -o $@ + backend_cuda.o: backend_cuda.cu backend_cuda.h backend_gpu_compat.h .build-config @command -v "$(GPUCC)" >/dev/null 2>&1 || { echo "$(GPUCC_NAME) not found" >&2; exit 1; } "$(GPUCC)" $(GPUFLAGS) -c backend_cuda.cu -o $@ @@ -339,8 +344,8 @@ cuda-bench: backend_cuda.cu backend_cuda.h backend_gpu_compat.h tests/bench_tens olmoe$(EXE): olmoe.c st.h json.h compat.h $(CC) $(CFLAGS) olmoe.c -o olmoe$(EXE) $(LDFLAGS) -inkling$(EXE): inkling.c st.h json.h compat.h - $(CC) $(CFLAGS) inkling.c -o inkling$(EXE) $(LDFLAGS) +inkling$(EXE): inkling.c st.h json.h compat.h $(INK_CUDA_OBJ) + $(CC) $(CFLAGS) inkling.c $(INK_CUDA_OBJ) -o inkling$(EXE) $(LDFLAGS) # Use a baseline that matches the compiler target. macOS already targets a # portable baseline when ARCH is empty; forcing the x86 value there breaks diff --git a/c/backend_cuda_ink.cu b/c/backend_cuda_ink.cu new file mode 100644 index 00000000..6839f5fe --- /dev/null +++ b/c/backend_cuda_ink.cu @@ -0,0 +1,87 @@ +/* CUDA backend for inkling.c — see backend_cuda_ink.h for scope. + * One warp per output row, both operands rounded to bf16, f32 accumulate: + * the same numeric contract as the CPU vdpbf16ps path (matmul_h), so GPU + * and CPU runs stay closely comparable. */ +#include +#include +#include +#include "backend_cuda_ink.h" + +static cudaStream_t g_st; +static float *g_dx = nullptr, *g_dy = nullptr; /* device activation buffers */ +static float *g_hx = nullptr, *g_hy = nullptr; /* PINNED host staging: pageable + * memcpyAsync degrades to a + * synchronous bounce copy */ +static size_t g_xcap = 0, g_ycap = 0; +static int g_ok = 0; + +extern "C" int ink_cuda_init(int dev) { + if (cudaSetDevice(dev) != cudaSuccess) return -1; + if (cudaStreamCreate(&g_st) != cudaSuccess) return -1; + /* fail fast on a broken runtime instead of at the first matmul */ + void *probe = nullptr; + if (cudaMalloc(&probe, 1 << 20) != cudaSuccess) return -1; + cudaFree(probe); + g_ok = 1; + return 0; +} + +extern "C" size_t ink_cuda_free_bytes(void) { + size_t fr = 0, to = 0; + if (cudaMemGetInfo(&fr, &to) != cudaSuccess) return 0; + return fr; +} + +extern "C" void *ink_cuda_upload(const void *h, size_t n) { + void *d = nullptr; + if (cudaMalloc(&d, n) != cudaSuccess) return nullptr; + if (cudaMemcpy(d, h, n, cudaMemcpyHostToDevice) != cudaSuccess) { cudaFree(d); return nullptr; } + return d; +} + +/* 4 warps per block, one output row per warp; lanes stride the contraction + * dim two bf16 at a time (4-byte coalesced loads of the weight row). */ +__global__ void mm_bf16_kernel(const __nv_bfloat16 * __restrict__ W, + const float * __restrict__ x, + float * __restrict__ y, int I, int O) { + int o = blockIdx.x * (blockDim.x >> 5) + (threadIdx.x >> 5); + int lane = threadIdx.x & 31; + int s = blockIdx.y; + if (o >= O) return; + const __nv_bfloat16 *w = W + (size_t)o * I; + const float *xs = x + (size_t)s * I; + float acc = 0.f; + for (int i = lane * 2; i < I; i += 64) { + __nv_bfloat162 wv = *(const __nv_bfloat162 *)(w + i); + float xa = __bfloat162float(__float2bfloat16(xs[i])); + float xb = __bfloat162float(__float2bfloat16(xs[i + 1])); + acc += __bfloat162float(wv.x) * xa + __bfloat162float(wv.y) * xb; + } + for (int off = 16; off; off >>= 1) acc += __shfl_down_sync(0xffffffffu, acc, off); + if (!lane) y[(size_t)s * O + o] = acc; +} + +extern "C" int ink_cuda_matmul_bf16(float *y, const float *x, const void *W, int S, int I, int O) { + if (!g_ok || (I & 1)) return -1; + size_t xn = (size_t)S * I * 4, yn = (size_t)S * O * 4; + if (xn > g_xcap) { + cudaFree(g_dx); cudaFreeHost(g_hx); + if (cudaMalloc(&g_dx, xn * 2) != cudaSuccess || + cudaMallocHost(&g_hx, xn * 2) != cudaSuccess) { g_dx = g_hx = nullptr; g_xcap = 0; return -1; } + g_xcap = xn * 2; + } + if (yn > g_ycap) { + cudaFree(g_dy); cudaFreeHost(g_hy); + if (cudaMalloc(&g_dy, yn * 2) != cudaSuccess || + cudaMallocHost(&g_hy, yn * 2) != cudaSuccess) { g_dy = g_hy = nullptr; g_ycap = 0; return -1; } + g_ycap = yn * 2; + } + memcpy(g_hx, x, xn); + cudaMemcpyAsync(g_dx, g_hx, xn, cudaMemcpyHostToDevice, g_st); + dim3 grid((unsigned)((O + 3) / 4), (unsigned)S); + mm_bf16_kernel<<>>((const __nv_bfloat16 *)W, g_dx, g_dy, I, O); + cudaMemcpyAsync(g_hy, g_dy, yn, cudaMemcpyDeviceToHost, g_st); + if (cudaStreamSynchronize(g_st) != cudaSuccess) return -1; + memcpy(y, g_hy, yn); + return 0; +} diff --git a/c/backend_cuda_ink.h b/c/backend_cuda_ink.h new file mode 100644 index 00000000..9383d2a5 --- /dev/null +++ b/c/backend_cuda_ink.h @@ -0,0 +1,23 @@ +/* Minimal CUDA backend for inkling.c: bf16 resident weights live in VRAM, + * matmuls run on-device. Motivation is bandwidth, not FLOPs: decode reads + * ~35 GB of bf16 residents per token, which caps CPU decode at the DDR5 + * bandwidth (~1.2 s/token); the GPU's VRAM feeds the same reads at ~12x. + * Deliberately tiny API — expert streaming, attention and everything else + * stay on the CPU in this phase. */ +#ifndef BACKEND_CUDA_INK_H +#define BACKEND_CUDA_INK_H +#include +#ifdef __cplusplus +extern "C" { +#endif + +int ink_cuda_init(int dev); /* 0 = ok */ +size_t ink_cuda_free_bytes(void); +void *ink_cuda_upload(const void *h, size_t n); /* NULL = OOM/error */ +/* y[S,O] = x[S,I] @ W^T, W = device bf16 [O,I]; x,y host f32. 0 = ok */ +int ink_cuda_matmul_bf16(float *y, const float *x, const void *W, int S, int I, int O); + +#ifdef __cplusplus +} +#endif +#endif diff --git a/c/inkling.c b/c/inkling.c index 85fda0ef..205f0ccc 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -32,6 +32,10 @@ #endif #include "st.h" #include "tok.h" +#ifdef COLI_CUDA +#include "backend_cuda_ink.h" +static int g_cuda = 0; +#endif #define MAXL 256 @@ -59,8 +63,11 @@ typedef struct { /* ---------- resident weights ---------- * Large matmul weights keep their on-disk dtype in RAM: bf16 for the real * 975B checkpoint (f32 residents would need ~172 GB, over sabre's 187), - * f32 for the tiny oracle (bit-exact validation). Exactly one pointer set. */ -typedef struct { float *f; uint16_t *h; } Wt; + * f32 for the tiny oracle (bit-exact validation). Under CUDA, bf16 tensors + * move to VRAM (dev set, host freed): decode reads ~35 GB of residents per + * token, so this trades the DDR5 bandwidth wall for VRAM bandwidth AND + * frees the same RAM for the expert cache. */ +typedef struct { float *f; uint16_t *h; void *dev; } Wt; typedef struct { float *in_ln, *post_ln; @@ -186,8 +193,14 @@ static void matmul_h(float *y, const float *x, const uint16_t *W, int S, int I, } } -/* dispatch on the weight's resident dtype */ +/* dispatch on where the weight lives */ static void matmul_w(float *y, const float *x, Wt W, int S, int I, int O) { +#ifdef COLI_CUDA + if (W.dev) { + if (ink_cuda_matmul_bf16(y, x, W.dev, S, I, O) == 0) return; + fprintf(stderr, "cuda matmul failed and host copy was freed\n"); exit(1); + } +#endif if (W.f) matmul(y, x, W.f, S, I, O); else matmul_h(y, x, W.h, S, I, O); } @@ -460,14 +473,25 @@ static void pread_all(int fd, void *buf, int64_t nb, int64_t off) { } /* big matmul weights keep their on-disk dtype resident: BF16 raw (real - * checkpoint, halves RAM), anything else as f32 (tiny oracle: bit-exact) */ -static Wt load_w(Model *m, const char *name) { + * checkpoint, halves RAM), anything else as f32 (tiny oracle: bit-exact). + * gpu_ok: bf16 tensors move to VRAM while budget lasts (embed stays host — + * it's a row lookup, not a matmul). */ +static Wt load_w(Model *m, const char *name, int gpu_ok) { Wt w = {0}; st_tensor *t = st_find(&m->S, name); if (!t) { fprintf(stderr, "missing %s\n", name); exit(1); } if (t->dtype == 0) { w.h = malloc(t->nbytes); if (!w.h) { fprintf(stderr,"OOM %s\n",name); exit(1); } pread_all(t->fd, w.h, t->nbytes, t->off); +#ifdef COLI_CUDA + /* keep 3 GB VRAM headroom for the activation buffers + future tiers */ + if (g_cuda && gpu_ok && ink_cuda_free_bytes() > (size_t)t->nbytes + (3ULL<<30)) { + w.dev = ink_cuda_upload(w.h, t->nbytes); + if (w.dev) { free(w.h); w.h = NULL; } + } +#else + (void)gpu_ok; +#endif } else { w.f = falloc(t->numel); st_read_f32(&m->S, name, w.f, 0); @@ -475,7 +499,8 @@ static Wt load_w(Model *m, const char *name) { return w; } static Wt wt_off(Wt w, int64_t off) { - Wt r = { w.f ? w.f + off : NULL, w.h ? w.h + off : NULL }; + Wt r = { w.f ? w.f + off : NULL, w.h ? w.h + off : NULL, + w.dev ? (char*)w.dev + off*2 : NULL }; /* dev is always bf16 */ return r; } static void wt_row_f32(Wt w, int64_t off, float *out, int n) { @@ -533,16 +558,26 @@ static void model_init(Model *m, const char *snap, int cap, int bits) { Cfg *c = &m->c; int D = c->hidden, K = c->conv_k; double t0 = now_s(); - m->embed = load_w(m, "model.embed_tokens.weight"); +#ifdef COLI_CUDA + if (!getenv("NOGPU")) { + int dev = getenv("GPU_DEV") ? atoi(getenv("GPU_DEV")) : 0; + if (ink_cuda_init(dev) == 0) { + g_cuda = 1; + fprintf(stderr, "[cuda] device %d ready, %.1f GB free — bf16 residents to VRAM\n", + dev, ink_cuda_free_bytes()/1e9); + } else fprintf(stderr, "[cuda] init failed, running on CPU\n"); + } +#endif + m->embed = load_w(m, "model.embed_tokens.weight", 0); m->embed_norm = st_has(&m->S,"model.embed_norm.weight") ? load_t(m,"model.embed_norm.weight") : NULL; m->final_norm = load_t(m, "model.norm.weight"); - m->lm_head = load_w(m, "lm_head.weight"); + m->lm_head = load_w(m, "lm_head.weight", 1); m->L = calloc(c->n_layers, sizeof(Layer)); char nm[320]; 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) - #define LDW(field, suffix) snprintf(nm,sizeof(nm),"model.layers.%d." suffix,i); l->field = load_w(m,nm) + #define LDW(field, suffix) snprintf(nm,sizeof(nm),"model.layers.%d." suffix,i); l->field = load_w(m,nm,1) LD(in_ln, "input_layernorm.weight"); LD(post_ln,"post_attention_layernorm.weight"); LDW(q, "self_attn.q_proj.weight"); LDW(k, "self_attn.k_proj.weight"); @@ -1081,7 +1116,10 @@ int main(int argc, char **argv) { * per-expert matmul regions are tiny and back-to-back; the default passive * wait policy parks the team between regions and re-wake latency dominates. * libgomp reads OMP_/GOMP_ vars before main(), so seed them and re-exec - * once (COLI_OMP_TUNED guards the exec; COLI_NO_OMP_TUNE=1 disables). */ + * once (COLI_OMP_TUNED guards the exec; COLI_NO_OMP_TUNE=1 disables). + * NOT under CUDA — same exception glm.c makes: a spinning 24-thread team + * starves the CUDA driver during every stream sync. */ +#ifndef COLI_CUDA if (!getenv("COLI_OMP_TUNED") && !getenv("COLI_NO_OMP_TUNE")) { setenv("OMP_WAIT_POLICY","active",0); setenv("GOMP_SPINCOUNT","200000",0); @@ -1093,6 +1131,7 @@ int main(int argc, char **argv) { perror("[OMP] execv self-reexec failed, running untuned"); #endif } +#endif /* !COLI_CUDA */ const char *snap = getenv("SNAP"); if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } /* flags: -p "prompt" [-n N] -> generate mode; positional: [cap] [bits] [ref.json] */ From d4f11459aa8d643fca11e7a55c81b30ea0762449 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 11:23:08 -0400 Subject: [PATCH 14/96] inkling: spin-sync device flag + phase timers MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit cudaDeviceScheduleSpin (+~1%: sync latency was not the bottleneck) and lightweight phase instrumentation in generate mode. First real breakdown on the 975B (24-token decode): fill 75.9s / expert-mm 10.4s / shared 0.9s / attn 1.2s — expert I/O is 85% of wall, and at 83.6% hit decode averages ~1 miss/layer, so fills run at queue depth 1: latency-bound, not bandwidth-bound. The GPU resident tier did its job; the remaining time belongs to cache hit rate and cross-layer prefetch. Co-Authored-By: Claude Fable 5 --- c/backend_cuda_ink.cu | 4 ++++ c/inkling.c | 12 ++++++++++++ 2 files changed, 16 insertions(+) diff --git a/c/backend_cuda_ink.cu b/c/backend_cuda_ink.cu index 6839f5fe..01fc935a 100644 --- a/c/backend_cuda_ink.cu +++ b/c/backend_cuda_ink.cu @@ -16,6 +16,10 @@ static size_t g_xcap = 0, g_ycap = 0; static int g_ok = 0; extern "C" int ink_cuda_init(int dev) { + /* ~727 tiny matmul calls per decoded token: the default yield-based sync + * costs ~0.5-1 ms of wakeup latency per call. Spin-sync in the driver is + * one busy thread — affordable since the OMP team no longer spins. */ + cudaSetDeviceFlags(cudaDeviceScheduleSpin); if (cudaSetDevice(dev) != cudaSuccess) return -1; if (cudaStreamCreate(&g_st) != cudaSuccess) return -1; /* fail fast on a broken runtime instead of at the first matmul */ diff --git a/c/inkling.c b/c/inkling.c index 205f0ccc..3de5ab45 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -109,6 +109,7 @@ typedef struct { uint32_t **eusage; /* per-layer expert selection counts */ int npin; /* pinned experts per sparse layer */ uint64_t clock, hits, miss; + double t_fill, t_expert, t_shared, t_attn, t_route; /* phase timers */ float **K, **V; int kv_len, max_t; /* per-layer [kv][max_t][hd] */ float **cs[4]; /* conv states, [n_layers][C*(K-1)] */ double dense_load_s; @@ -943,8 +944,10 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { } /* pass 2: one parallel burst for every miss in this layer call */ if (nfill) { + double tf = now_s(); #pragma omp parallel for schedule(dynamic,1) for (int j = 0; j < nfill; j++) slot_fill(m, fl[j], fill[j]); + m->t_fill += now_s() - tf; } /* pass 3: compute. gate+up run as ONE matmul over the fused 2I rows — * halves the number of (expensive to open) parallel regions per expert */ @@ -954,6 +957,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { const float *xs = x + (int64_t)s*D; float *os = out + (int64_t)s*D; float *w = wgt + (int64_t)s*(K+ns); + double te = now_s(); for (int kk = 0; kk < K; kk++) { Slot *e = use[(int64_t)s*K + kk]; if (m->xq) { @@ -977,6 +981,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { } for (int d = 0; d < D; d++) os[d] += w[kk] * hh[d]; } + double ts = now_s(); m->t_expert += ts - te; /* shared experts: gamma inside (before down_proj is linear, so applied at the end) */ for (int j = 0; j < ns; j++) { matmul_w(g, xs, wt_off(l->sh_g, (int64_t)j*I*D), 1, D, I); @@ -985,6 +990,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out) { matmul_w(hh, g, wt_off(l->sh_d, (int64_t)j*D*I), 1, I, D); for (int d = 0; d < D; d++) os[d] += w[K+j] * hh[d]; } + m->t_shared += now_s() - ts; } free(logits); free(idx); free(wgt); free(use); free(fill); free(fl); free(g); free(hh); /* u aliases g+I */ @@ -1004,7 +1010,9 @@ static float *step(Model *m, const int *ids, int S, int pos0, int *tf_out) { 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); + double ta = now_s(); attention(m, l, i, nrm, S, pos0, tmp); + m->t_attn += now_s() - ta; sconv_apply(tmp, S, D, l->a_cw, m->cs[2][i], c->conv_k); for (int64_t j = 0; j < (int64_t)S*D; j++) x[j] += tmp[j]; 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); @@ -1099,6 +1107,10 @@ static void generate_stream(Model *m, Tok *T, const char *prompt, int n_new) { int gen = len - np; printf("\n[prefill %.1fs | %d tokens in %.1fs = %.2f tok/s | RSS %.1f GB]\n", t1 - t0, gen, dt, gen > 1 ? (gen-1)/dt : 0.0, rss_gb()); + double wall = now_s() - t0; + printf("[phases] fill %.1fs | expert-mm %.1fs | shared %.1fs | attn %.1fs | other %.1fs\n", + m->t_fill, m->t_expert, m->t_shared, m->t_attn, + wall - m->t_fill - m->t_expert - m->t_shared - m->t_attn); free(ids); } From d2fc927864db58cb4064729e5e89b9777a604787 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 11:28:18 -0400 Subject: [PATCH 15/96] inkling: default PIN_N to cap/2 (measured: pinning is the whole ballgame) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 975B decode by pin depth, same auto cap 79/layer: 19 pinned = 0.32 tok/s (83.6% hit), 40 = 0.80 (95.6%), 64 = 2.51 tok/s at 100% hit on a usage-trained prompt — decode fills run at queue depth ~1, so every pinned expert removes a ~35ms stall. Phase profile inverted: expert I/O fell from 85% of wall to ~2%, CPU expert matmul is now 90%. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/c/inkling.c b/c/inkling.c index 3de5ab45..dc725107 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -745,7 +745,10 @@ static void pins_load(Model *m, const char *snap) { fclose(f); return; } int cap = m->cache[0].cap; - m->npin = getenv("PIN_N") ? atoi(getenv("PIN_N")) : cap/4; + /* default: pin half the cap. Measured on the 975B: cap/4 (19/layer) gave + * 83.6% hit / 0.32 tok/s; 40/layer gave 95.6% / 0.80 tok/s — decode fills + * run at queue depth ~1, so every pinned expert removes a ~35ms stall. */ + m->npin = getenv("PIN_N") ? atoi(getenv("PIN_N")) : cap/2; if (m->npin > cap - 8) m->npin = cap - 8; if (m->npin < 0) m->npin = 0; uint32_t *tmp = malloc((size_t)E * 4); From deb3bd072e5283233a4ffa9366257ae4a3258809 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 11:42:29 -0400 Subject: [PATCH 16/96] inkling: -f promptfile mode for usage warmup (one load, many prompts) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit One prompt per line (# comments skipped), model loaded once, KV/conv state reset between prompts, usage history accumulated across all of them and saved at the end. Purpose: build a routing ranking that generalizes — pins trained on one prompt hit 100% on it but 79.8% on novel routing (and crowd out LRU capacity), so the history needs diverse coverage before deep pinning pays off. kv_alloc now reuses its buffers across prompts instead of leaking per call. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index dc725107..a0af98b7 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -1055,6 +1055,9 @@ static void state_reset(Model *m) { static void kv_alloc(Model *m, int max_t) { Cfg *c = &m->c; + if (m->K && max_t <= m->max_t) return; /* reuse across prompts when big enough */ + if (m->K) for (int i = 0; i < c->n_layers; i++) { free(m->K[i]); free(m->V[i]); } + free(m->K); free(m->V); m->max_t = max_t; 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++) { @@ -1150,26 +1153,39 @@ int main(int argc, char **argv) { const char *snap = getenv("SNAP"); if (!snap) { fprintf(stderr, "set SNAP=\n"); return 1; } /* flags: -p "prompt" [-n N] -> generate mode; positional: [cap] [bits] [ref.json] */ - const char *prompt = NULL, *refpath = "ref_inkling.json"; + const char *prompt = NULL, *pfile = NULL, *refpath = "ref_inkling.json"; int cap = -1, bits = 0, n_new = 256, npos = 0; for (int i = 1; i < argc; i++) { if (!strcmp(argv[i], "-p") && i+1 < argc) prompt = argv[++i]; + else if (!strcmp(argv[i], "-f") && i+1 < argc) pfile = argv[++i]; else if (!strcmp(argv[i], "-n") && i+1 < argc) n_new = atoi(argv[++i]); else if (npos == 0) { cap = atoi(argv[i]); npos++; } else if (npos == 1) { bits = atoi(argv[i]); npos++; } else refpath = argv[i]; } - if (cap < 0) cap = prompt ? 0 : 16; /* generate mode defaults to RAM-sized auto cap */ + if (cap < 0) cap = (prompt || pfile) ? 0 : 16; /* generate mode defaults to RAM-sized auto cap */ if (bits && (bits < 2 || bits > 8)) { fprintf(stderr, "quant_bits must be 0 (f32) or 2..8\n"); return 1; } - if (prompt) { + if (prompt || pfile) { Model m; model_init(&m, snap, cap, bits); printf("== Inkling C engine, %d layers, experts @ %s, cache %d/layer ==\n", m.c.n_layers, m.xq ? "container" : bits ? "int" : "f32", m.cache[0].cap); pins_load(&m, snap); char tkp[2048]; snprintf(tkp, sizeof(tkp), "%s/tokenizer.json", snap); Tok T; tok_load(&T, tkp); - generate_stream(&m, &T, prompt, n_new); + if (prompt) generate_stream(&m, &T, prompt, n_new); + else { /* -f: one prompt per line, model loaded once, usage accumulates */ + FILE *pf = fopen(pfile, "rb"); if (!pf) { perror(pfile); return 1; } + char ln[8192]; int np = 0; + while (fgets(ln, sizeof(ln), pf)) { + size_t n = strlen(ln); while (n && (ln[n-1]=='\n'||ln[n-1]=='\r')) ln[--n]=0; + if (!n || ln[0]=='#') continue; + printf("\n===== prompt %d =====\n", ++np); + state_reset(&m); + generate_stream(&m, &T, ln, n_new); + } + fclose(pf); + } usage_save(&m, snap); double tot = m.hits + m.miss; printf("[cache] hit %.1f%% (%llu hit / %llu load) | usage history saved\n", From 9210904898c97e29112f571bd8148b223b631b34 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 11:48:01 -0400 Subject: [PATCH 17/96] inkling: make cache warming toggleable MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit PIN=off (or PIN=0) disables warming entirely — no usage seeding, no startup pinning, cold LRU start. PIN_N=0 keeps the seeded ranking but pins nothing. USAGE_SAVE=0 skips the end-of-run history rewrite so benchmark loops don't skew the accumulated ranking; PIN=off implies it. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/c/inkling.c b/c/inkling.c index a0af98b7..848dab29 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -729,11 +729,18 @@ static void slot_fill(Model *m, int layer, Slot *s) { /* pin the top-N experts per sparse layer from a usage-history file (colibri * .coli_usage convention: one uint32 count per expert per layer). Pins are - * regular cache slots flagged non-evictable, filled in parallel at startup. */ + * regular cache slots flagged non-evictable, filled in parallel at startup. + * Toggles: PIN=off (or PIN=0) skips cache warming entirely (no seeding, no + * pins, cold LRU start); PIN_N=0 seeds the ranking from the history but pins + * nothing; PIN= uses an alternate history file; PIN_N= pin depth. */ static void pins_load(Model *m, const char *snap) { Cfg *c = &m->c; int E = c->n_experts; char up[2048]; const char *env = getenv("PIN"); + if (env && (!strcmp(env, "off") || !strcmp(env, "0"))) { + fprintf(stderr, "[pin] cache warming disabled (PIN=%s)\n", env); + return; + } if (env) snprintf(up, sizeof(up), "%s", env); else snprintf(up, sizeof(up), "%s/.coli_usage", snap); FILE *f = fopen(up, "rb"); @@ -784,11 +791,16 @@ static void pins_load(Model *m, const char *snap) { } /* usage snapshot: rewritten after every generation run (same contract as - * glm's .coli_usage — copy it aside if you need a stable ranking) */ + * glm's .coli_usage — copy it aside if you need a stable ranking). + * USAGE_SAVE=0 skips the rewrite (e.g. benchmark loops that would skew the + * ranking); PIN=off also implies no save (that run never seeded counts). */ static void usage_save(Model *m, const char *snap) { Cfg *c = &m->c; int E = c->n_experts; char up[2048], tp[2060]; const char *env = getenv("PIN"); + const char *sv = getenv("USAGE_SAVE"); + if (sv && *sv == '0') return; + if (env && (!strcmp(env, "off") || !strcmp(env, "0"))) return; if (env) snprintf(up, sizeof(up), "%s", env); else snprintf(up, sizeof(up), "%s/.coli_usage", snap); snprintf(tp, sizeof(tp), "%s.tmp", up); From 6ebac549633da3cb54e5afd719f9dce3ca393f96 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Thu, 16 Jul 2026 12:20:23 -0400 Subject: [PATCH 18/96] inkling: docs/inkling.md + honest usage-save reporting MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Quickstart, modes, cache-warming env reference, the full benchmark table (Stage A 0.06 -> trained-pins 2.51 tok/s, steady state 0.25 at 82.2% hit on a novel prompt with an 11-prompt warmup history), and the validation story. usage_save now reports whether it actually wrote — the message printed 'saved' even under USAGE_SAVE=0. Co-Authored-By: Claude Fable 5 --- c/inkling.c | 17 ++++----- docs/inkling.md | 95 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 8 deletions(-) create mode 100644 docs/inkling.md diff --git a/c/inkling.c b/c/inkling.c index 848dab29..5f5fa692 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -794,25 +794,25 @@ static void pins_load(Model *m, const char *snap) { * glm's .coli_usage — copy it aside if you need a stable ranking). * USAGE_SAVE=0 skips the rewrite (e.g. benchmark loops that would skew the * ranking); PIN=off also implies no save (that run never seeded counts). */ -static void usage_save(Model *m, const char *snap) { +static int usage_save(Model *m, const char *snap) { Cfg *c = &m->c; int E = c->n_experts; char up[2048], tp[2060]; const char *env = getenv("PIN"); const char *sv = getenv("USAGE_SAVE"); - if (sv && *sv == '0') return; - if (env && (!strcmp(env, "off") || !strcmp(env, "0"))) return; + if (sv && *sv == '0') return 0; + if (env && (!strcmp(env, "off") || !strcmp(env, "0"))) return 0; if (env) snprintf(up, sizeof(up), "%s", env); else snprintf(up, sizeof(up), "%s/.coli_usage", snap); snprintf(tp, sizeof(tp), "%s.tmp", up); FILE *f = fopen(tp, "wb"); - if (!f) return; + if (!f) return 0; uint32_t hdr[3] = { 0x31554B49u, (uint32_t)c->n_layers, (uint32_t)E }; fwrite(hdr, 4, 3, f); uint32_t *zero = calloc(E, 4); for (int i = 0; i < c->n_layers; i++) fwrite(m->eusage[i] ? m->eusage[i] : zero, 4, E, f); free(zero); fclose(f); - rename(tp, up); + return rename(tp, up) == 0; } /* ---------- attention (GQA + sliding/global + relative bias + K/V sconv) ---------- */ @@ -1198,11 +1198,12 @@ int main(int argc, char **argv) { } fclose(pf); } - usage_save(&m, snap); + int saved = usage_save(&m, snap); double tot = m.hits + m.miss; - printf("[cache] hit %.1f%% (%llu hit / %llu load) | usage history saved\n", + printf("[cache] hit %.1f%% (%llu hit / %llu load)%s\n", tot ? 100.0*m.hits/tot : 0.0, - (unsigned long long)m.hits, (unsigned long long)m.miss); + (unsigned long long)m.hits, (unsigned long long)m.miss, + saved ? " | usage history saved" : ""); return 0; } diff --git a/docs/inkling.md b/docs/inkling.md new file mode 100644 index 00000000..f86028a6 --- /dev/null +++ b/docs/inkling.md @@ -0,0 +1,95 @@ +# Inkling (Thinking Machines 975B MoE) on colibri + +`c/inkling.c` runs [Thinking Machines Inkling](https://huggingface.co/thinkingmachines/Inkling) +(975B total / 41B active, Apache 2.0) with colibri's expert-streaming approach: +dense weights resident (RAM or VRAM), routed experts streamed from disk with an +LRU + pinned cache. Text-only; vision/audio encoders and the MTP head are not loaded. + +## Quickstart + +Pre-converted weights (int4 experts + bf16 residents, ~469 GiB): + +```sh +hf download nbeerbower/Inkling-colibri-int4 --local-dir ~/Models/inkling_i4 +``` + +or convert the original bf16 checkpoint yourself (shard-resumable; `--watch` +converts while the download is still running): + +```sh +python3 c/tools/convert_inkling_int4.py --indir --outdir ~/Models/inkling_i4 +``` + +Build and run: + +```sh +make -C c inkling # pure CPU (dependency-free, like glm) +make -C c inkling CUDA=1 # + bf16 residents in VRAM (needs ~37 GB free) + +SNAP=~/Models/inkling_i4 ./c/inkling -p "The capital of France is" -n 64 +``` + +Requirements: ~120 GB RAM (CPU build keeps ~86 GB of bf16 residents in RAM; +the CUDA build moves them to VRAM and uses the freed RAM for a larger expert +cache), NVMe storage for the snapshot. + +## Modes + +| Invocation | What it does | +|---|---| +| `-p "text" [-n N]` | streaming greedy generation (stops at eos or N tokens) | +| `-f prompts.txt [-n N]` | one prompt per line (`#` comments skipped), single model load, state reset between prompts — the cache-warming workflow below | +| `[cap] [bits] [ref.json]` | token-exact oracle harness against a `tools/make_tiny_inkling.py` fixture (CI-style validation) | + +## Cache warming (same idea as glm's `.coli_usage`) + +Expert selections are counted per `(layer, expert)` and written to +`SNAP/.coli_usage` after each generation run. On startup the top `PIN_N` +experts per layer are pinned (non-evictable, loaded in one parallel burst). +Counts **accumulate across runs**, so the ranking converges toward your real +workload — pins trained on a single prompt overfit badly (see the benchmark +table), which is why a diverse warmup matters: + +```sh +SNAP=~/Models/inkling_i4 ./c/inkling -f warmup_prompts.txt -n 32 +``` + +| Env | Effect | +|---|---| +| `PIN=off` | disable warming entirely: no seeding, no pins, no history rewrite | +| `PIN=` | alternate history file | +| `PIN_N=` | pins per layer (default `cap/2`; 0 = seed ranking only) | +| `USAGE_SAVE=0` | don't rewrite the history (benchmark runs) | +| `NOGPU=1` / `GPU_DEV=` | disable CUDA / select device | +| `IDOT=0` | byte-exact scalar int kernels (debugging) | +| First positional arg | expert-cache cap per layer (`0` = auto-size from free RAM) | + +## Performance (975B, Ryzen 9 7900 / 24t, 187 GB DDR5, RTX A6000, NVMe) + +24-token greedy decode, 5-token prompt, commit-tagged runs, single run each. +"Trained" = usage history built from the same prompt; "novel" = never-seen prompt. + +| Configuration | Prefill | Decode | Cache hit | +|---|---|---|---| +| Stage A (plain LRU, serial I/O, CPU) | 150.2 s | 0.06 tok/s | ~0% | +| + packed-int4 cache, parallel fills, pins (CPU) | 21.1 s | 0.25 tok/s | 81.5% | +| + CUDA resident tier (A6000) | 18.4 s | 0.32 tok/s | 83.6% | +| + deep pins, trained prompt (`PIN_N=64`) | 1.9 s | 2.51 tok/s | 100.0% | +| deep pins, novel prompt (overfit pins) | 33.8 s | 0.17 tok/s | 79.8% | +| **steady state: 11-prompt diverse history, default pins, novel prompt** ¹ | **35.4 s** | **0.25 tok/s** | **82.2%** | + +¹ 48-token generation, 13-token prompt. With only a small warmup corpus the +ranking is barely ahead of plain LRU; hit rate (and therefore decode speed) +grows toward the trained-prompt number as real-use history accumulates. + +Phase profile at high hit rates: ~90% CPU expert matmul — the next lever is +expert compute on the GPU, not more I/O work. + +## Validation + +Every mode is token-exact against HF transformers on a tiny random-init oracle +(`c/tools/make_tiny_inkling.py`): f32, int4-container (VNNI and `IDOT=0` +scalar), bf16 residents on CPU, and bf16 residents through the CUDA kernel. +The tokenizer (o200k family, auto-detected by `tok.h`) encodes 357/357 test +strings identically to HF `tokenizers`. The converter round-trips a fabricated +TML-layout checkpoint back through the engine exactly (`--selftest-e2e`). From 2c396d821bb20e229b91515339abc6134571bb1a Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 22 Jul 2026 20:27:12 -0400 Subject: [PATCH 19/96] ci: build inkling + token-exact oracle in CI MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A second engine that CI doesn't compile rots within weeks, so: - the Engine (Linux, CPU) job now `make colibri inkling` — inkling is built on every push/PR alongside the flagship; - the CUDA syntax-check job also `nvcc -c backend_cuda_ink.cu` (no-GPU syntax gate, same no-pipeline rule so it can actually fail); - a new `inkling-oracle` job builds a tiny random-init InklingForCausalLM via HF transformers (tools/make_tiny_inkling.py) and has the C engine reproduce it token-for-token (bits=0, bit-exact experts). The greedy pass exits non-zero on any mismatch, so the gate fails the moment a shared-code change (tok.h, Makefile) regresses the engine — the same guarantee glm_tiny gives colibri. oracle-requirements.txt pins CPU torch + transformers>=5.14 (the first release with Inkling support); setup-python hashes it for the pip cache. Left out of the dependency-free `make check` gate on purpose: that runs on Windows/macOS too, and this oracle needs torch/transformers. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_016KLDCYJSyHxd39ChTr4T27 --- .github/workflows/ci.yml | 36 +++++++++++++++++++++++++++++++-- .gitignore | 1 + c/tools/oracle-requirements.txt | 9 +++++++++ 3 files changed, 44 insertions(+), 2 deletions(-) create mode 100644 c/tools/oracle-requirements.txt diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 426ea413..a21bd4f1 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,8 +12,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: Build colibri - run: cd c && make colibri + - name: Build colibri + inkling + run: cd c && make colibri inkling - name: C test suite run: cd c && make test-c @@ -40,6 +40,38 @@ jobs: nvcc -O2 -std=c++17 -arch=sm_80 -c backend_cuda.cu -o /dev/null \ -Xcompiler=-Wall,-Wextra echo "CUDA syntax check passed" + - name: Compile backend_cuda_ink.cu (syntax only, no GPU) + run: | + cd c + # inkling's own CUDA backend (bf16 residents + int4 expert GEMM); same + # no-pipeline rule as above so a compile error can actually fail the job. + nvcc -O2 -std=c++17 -arch=sm_80 -c backend_cuda_ink.cu -o /dev/null \ + -Xcompiler=-Wall,-Wextra + echo "inkling CUDA syntax check passed" + + inkling-oracle: + name: Inkling oracle (token-exact vs transformers) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: pip + cache-dependency-path: c/tools/oracle-requirements.txt + - name: Install torch (CPU) + transformers + run: pip install -r c/tools/oracle-requirements.txt + - name: Build inkling + run: cd c && make inkling + - name: Tiny-model fixture + token-exact oracle (f32, bit-exact experts) + run: | + cd c + # Random-init InklingForCausalLM via HF transformers, then the C engine + # reproduces it token-for-token. bits=0 keeps experts f32 (bit-exact + # forward); the greedy pass exits non-zero on any mismatch, so this + # gate fails the moment a shared-code change regresses the engine. + python3 tools/make_tiny_inkling.py tiny_inkling + SNAP=tiny_inkling ./inkling 8 0 tiny_inkling/ref_inkling.json engine-hip-syntax: name: HIP syntax check diff --git a/.gitignore b/.gitignore index b6866801..7ac3789d 100644 --- a/.gitignore +++ b/.gitignore @@ -69,3 +69,4 @@ olmoe_merged/ olmoe_i4/ c/olmoe_merged/ c/olmoe_i4/ +c/tiny_inkling/ diff --git a/c/tools/oracle-requirements.txt b/c/tools/oracle-requirements.txt new file mode 100644 index 00000000..75a1e88d --- /dev/null +++ b/c/tools/oracle-requirements.txt @@ -0,0 +1,9 @@ +# Deps for the Inkling tiny-model oracle (tools/make_tiny_inkling.py) — the +# ci.yml `inkling-oracle` job installs exactly this file, and setup-python +# hashes it for the pip cache key. CPU torch: the oracle is a tiny random-init +# model, no GPU wanted. +--index-url https://download.pytorch.org/whl/cpu +--extra-index-url https://pypi.org/simple +torch +transformers>=5.14 # Inkling architecture support (InklingForCausalLM) +safetensors From 182b6d4396b596e9369b809e546b5cd8d39b14a6 Mon Sep 17 00:00:00 2001 From: Maxwell D Collins Date: Wed, 22 Jul 2026 18:19:10 -0700 Subject: [PATCH 20/96] Remove google redirect from links This change follows up on c5b9d5a. (https://github.com/JustVugg/colibri/pull/504) It removes an unnecessary `https://www.google.com/search?q=` from links in `docker/README.md`. This fixes one more anchor (on line 395), outside the TOC anchors addressed in PR 504. And even for external links it seems better to just point directly at the relevant page rather than a Google result. --- docker/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docker/README.md b/docker/README.md index 0fc19f1f..9f5800c0 100644 --- a/docker/README.md +++ b/docker/README.md @@ -52,11 +52,11 @@ Additionally: a **fast SSD** is essential. Colibrì uses the disk as additional ### Software -* **Docker Desktop** (Windows, Mac, Linux) — [download here](https://www.google.com/search?q=https://www.docker.com/products/docker-desktop/) +* **Docker Desktop** (Windows, Mac, Linux) — [download here](https://www.docker.com/products/docker-desktop/) * **Python** (only if you want to download the model yourself) -* Windows: [python.org](https://www.google.com/search?q=https://www.python.org) or Microsoft Store +* Windows: [python.org](https://www.python.org) or Microsoft Store * Linux: `apt-get install python3 python3-pip` -* Mac: [python.org](https://www.google.com/search?q=https://www.python.org) or Homebrew +* Mac: [python.org](https://www.python.org) or Homebrew No build environment is needed. Everything happens inside the Docker container. @@ -90,14 +90,14 @@ hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir . If you are on Windows and cannot get it to work with Python: -* Download manually from [Hugging Face](https://www.google.com/search?q=https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +* Download manually from [Hugging Face](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) * Unzip into a folder (e.g., `C:\LLM\models\glm-5.2`) --- ### Step 2: Download the Colibrì Dockerfile -1. Go to: [https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile](https://www.google.com/search?q=https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile) +1. Go to: [https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile](https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile) 2. Click the **Download** button (⬇️ icon) in the top right 3. Save the file in a folder (e.g., `C:\LLM\Colibrì`) @@ -229,7 +229,7 @@ Now you are inside Linux. Type `exit` to leave. **Solution**: -1. Reinstall [Docker Desktop](https://www.google.com/search?q=https://www.docker.com/products/docker-desktop/) +1. Reinstall [Docker Desktop](https://www.docker.com/products/docker-desktop/) 2. Restart your computer 3. Open a new terminal and try again @@ -371,7 +371,7 @@ Then retry the `hf_download` command. **Solution**: 1. Wait and retry the `hf_download` command -2. If it continues, download manually from [here](https://www.google.com/search?q=https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +2. If it continues, download manually from [here](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) 3. Unzip the ZIP file into the desired folder --- @@ -392,7 +392,7 @@ If your disk is slow, the answers will be very slow even with a lot of RAM. ### Recommended default configuration for WLS on Windows -If you have **exactly 32 GB of RAM** and are using Windows, it is very likely that WLS by default is set to consume no more than 16 GB of RAM. We need to increase this limit [Troubleshooting](https://www.google.com/search?q=#troubleshooting) . In my case I adopted this configuration: +If you have **exactly 32 GB of RAM** and are using Windows, it is very likely that WLS by default is set to consume no more than 16 GB of RAM. We need to increase this limit [Troubleshooting](#troubleshooting) . In my case I adopted this configuration: ```ini [wsl2] From 23545400826a407e5887ab30da9c846fa055f2d7 Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 22 Jul 2026 22:06:13 -0400 Subject: [PATCH 21/96] inkling: serve mode (openai_server.py engine protocol) + sampling Stage-A inkling.c only had greedy -p / oracle modes, so the shared openai_server.py gateway had nothing to drive. Add the serve loop it speaks, byte-identical to colibri.c's protocol: - SERVE=1 -> serve_loop: READY sentinel, STAT handshake, then per request SUBMIT -> DATA frames -> DONE STAT ... + PROF (per-turn phase timings). CANCEL honored between tokens; requests run one at a time (v1). - temperature + top-p nucleus sampling (sample_logits; temp<=0 = greedy, so the oracle path is unchanged) + a light REP_PEN repeat guard. - HWINFO / TIERS / EMAP dashboard lines (same as colibri.c) so the web Brain/Profiling pages render live expert-tier state. - prompt_reject bounds the served context (CTX_MAX, default 8192). Additive: the oracle still matches 36/36 teacher-forced + 24/24 greedy, 0 warnings. POSIX serve loop (inkling targets Linux/CUDA); the select() include is guarded like colibri.c's. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_016KLDCYJSyHxd39ChTr4T27 --- c/inkling.c | 246 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 246 insertions(+) diff --git a/c/inkling.c b/c/inkling.c index 5f5fa692..390c217c 100644 --- a/c/inkling.c +++ b/c/inkling.c @@ -29,6 +29,7 @@ #include #if defined(__APPLE__) || defined(__linux__) || defined(__FreeBSD__) #include +#include /* serve-loop stdin poll (POSIX); inkling serves on Linux */ #endif #include "st.h" #include "tok.h" @@ -1132,6 +1133,239 @@ static void generate_stream(Model *m, Tok *T, const char *prompt, int n_new) { free(ids); } +/* ---------- serve mode: openai_server.py engine protocol ---------- + * stdin: SUBMIT \n\n + * CANCEL \n + * stdout: READY sentinel once loaded, then per request a stream of + * DATA \n\n frames and a final + * DONE STAT \n + * Byte-identical to colibri.c's serve protocol so the shared openai_server.py + * gateway drives inkling unchanged (v1: one request at a time; the KV slot arg + * is accepted but every request re-prefills). */ + +static uint64_t g_rng = 0x9E3779B97F4A7C15ull; +static double rng_next(void) { + g_rng ^= g_rng << 13; g_rng ^= g_rng >> 7; g_rng ^= g_rng << 17; + return (double)(g_rng >> 11) / 9007199254740992.0; +} + +/* temperature + top-p nucleus sampling; temp<=0 = greedy (the oracle path) */ +typedef struct { float p; int i; } PI; +static int pi_desc(const void *a, const void *b) { + float d = ((const PI*)b)->p - ((const PI*)a)->p; + return d > 0 ? 1 : d < 0 ? -1 : 0; +} +static int sample_logits(const float *logit, int n, float temp, float top_p) { + int best = 0; + for (int i = 1; i < n; i++) if (logit[i] > logit[best]) best = i; + if (temp <= 0.f) return best; + PI *c = malloc((size_t)n * sizeof(PI)); + double sum = 0; + for (int i = 0; i < n; i++) { + c[i].p = expf((logit[i] - logit[best]) / temp); + c[i].i = i; sum += c[i].p; + } + qsort(c, n, sizeof(PI), pi_desc); + double cut = (top_p > 0.f && top_p < 1.f) ? top_p * sum : sum; + double acc = 0; int k = 0; + while (k < n && acc < cut) acc += c[k++].p; + double r = rng_next() * acc, run = 0; + int pick = c[0].i; + for (int i = 0; i < k; i++) { run += c[i].p; if (run >= r) { pick = c[i].i; break; } } + free(c); + return pick; +} + +/* light repeat guard: recently emitted tokens get their logit divided by pen>1 */ +static void apply_rep_penalty(float *logit, int n, const int *hist, int nhist, float pen) { + if (pen <= 1.f) return; + for (int i = 0; i < nhist; i++) { + int t = hist[i]; + if (t < 0 || t >= n) continue; + logit[t] = logit[t] > 0 ? logit[t] / pen : logit[t] * pen; + } +} + +/* reject a prompt that would overrun the served KV bound (CTX_MAX, default 8192) */ +static const char *prompt_reject(int np, int want) { + const char *cm = getenv("CTX_MAX"); + int ctx_max = cm ? atoi(cm) : 8192; + if (np + want > ctx_max) return "context exceeds CTX_MAX"; + return NULL; +} + +typedef struct { char id[64]; int max_tok; float temp, top_p; char *payload; int plen; } SReq; +#define SRV_QMAX 16 +static SReq g_q[SRV_QMAX]; static int g_qn = 0; + +static int stdin_readable(void) { + fd_set r; struct timeval tv = {0, 0}; + FD_ZERO(&r); FD_SET(0, &r); + return select(1, &r, NULL, NULL, &tv) > 0; +} + +/* read one control line (+ payload for SUBMIT). cur_id: request in flight; + * returns 1 if that request was cancelled, 0 otherwise, -1 on stdin EOF. */ +static int serve_read_cmd(const char *cur_id) { + char ln[512]; + if (!fgets(ln, sizeof(ln), stdin)) return -1; + char cmd[16], id[64]; + if (sscanf(ln, "%15s %63s", cmd, id) < 2) return 0; + if (!strcmp(cmd, "CANCEL")) return cur_id && !strcmp(id, cur_id); + if (!strcmp(cmd, "SUBMIT")) { + int slot, plen, max_tok; float temp, top_p; + if (sscanf(ln, "%*s %*s %d %d %d %f %f", &slot, &plen, &max_tok, &temp, &top_p) != 5 || + plen < 0 || plen > (1<<22)) { printf("ERROR %s bad submit header\n", id); fflush(stdout); return 0; } + (void)slot; + char *pl = malloc((size_t)plen + 1); + if (fread(pl, 1, (size_t)plen, stdin) != (size_t)plen) { free(pl); return -1; } + int nl = fgetc(stdin); (void)nl; + pl[plen] = 0; + if (g_qn < SRV_QMAX) { + SReq *q = &g_q[g_qn++]; + snprintf(q->id, sizeof(q->id), "%s", id); + q->max_tok = max_tok; q->temp = temp; q->top_p = top_p; + q->payload = pl; q->plen = plen; + } else { printf("ERROR %s queue full\n", id); fflush(stdout); free(pl); } + } + return 0; +} + +static void serve_one(Model *m, Tok *T, SReq *q) { + Cfg *c = &m->c; + int cap = q->plen + 16; + int *ids = malloc((size_t)cap * sizeof(int)); + int np = tok_encode(T, q->payload, q->plen, ids, cap); + if (np <= 0) { printf("ERROR %s empty prompt\n", q->id); fflush(stdout); free(ids); return; } + const char *bad = prompt_reject(np, q->max_tok); + if (bad) { printf("ERROR %s %s\n", q->id, bad); fflush(stdout); free(ids); return; } + state_reset(m); + kv_alloc(m, np + q->max_tok + 8); + double t0 = now_s(); + uint64_t h0 = m->hits, m0 = m->miss; + /* per-turn phase snapshot for the PROF line (timers accumulate globally) */ + double f0 = m->t_fill, e0 = m->t_expert, s0 = m->t_shared, a0 = m->t_attn; + float *logit = step(m, ids, np, 0, NULL); + int len = np, gen = 0, limited = 1, cancelled = 0; + char buf[512]; + /* repetition-penalty history: prompt tail + emitted tokens, ring of 128 */ + float rep = getenv("REP_PEN") ? atof(getenv("REP_PEN")) : 1.1f; + int hist[128], nhist = 0; + for (int i = (np > 128 ? np - 128 : 0); i < np; i++) hist[nhist++] = ids[i]; + for (int s = 0; s < q->max_tok && !cancelled; s++) { + apply_rep_penalty(logit, c->unpad_vocab, hist, nhist, rep); + int tk = sample_logits(logit, c->unpad_vocab, q->temp, q->top_p); + free(logit); logit = NULL; + if (tk == c->eos) { limited = 0; break; } + if (nhist < 128) hist[nhist++] = tk; + else { memmove(hist, hist+1, 127*sizeof(int)); hist[127] = tk; } + int nb = tok_decode(T, &tk, 1, buf, sizeof(buf)-1); + printf("DATA %s %d\n", q->id, nb); + fwrite(buf, 1, (size_t)nb, stdout); + fputc('\n', stdout); fflush(stdout); + gen++; len++; + while (stdin_readable()) { + int r = serve_read_cmd(q->id); + if (r < 0) { free(ids); return; } + if (r > 0) { cancelled = 1; limited = 0; } + } + if (cancelled || s == q->max_tok - 1) break; + logit = step(m, &tk, 1, len - 1, NULL); + } + free(logit); + double dt = now_s() - t0; + double tot = (double)(m->hits - h0 + m->miss - m0); + printf("DONE %s STAT %d %.3f %.1f %.2f %d %d\n", q->id, gen, + dt > 0 ? gen/dt : 0.0, tot ? 100.0*(m->hits-h0)/tot : 0.0, rss_gb(), np, limited); + /* PROF: per-turn phase timings for the dashboard (gateway schema — we map + * expert_wait -> shared-expert compute, lm_head folded into 0). */ + printf("PROF %.3f %d %d %.3f %.3f %.3f %.3f %.3f %d\n", dt, np, gen, + m->t_fill - f0, m->t_shared - s0, m->t_expert - e0, m->t_attn - a0, 0.0, gen + 1); + fflush(stdout); + free(ids); +} + +/* ---------- dashboard protocol (HWINFO / TIERS / EMAP) ---------- + * Same stdout lines colibri.c emits for the web dashboard; the gateway parses + * them and the Brain/Profiling pages render live expert-tier state. */ +static void serve_hwinfo(Model *m) { + char cpu[256] = ""; int cores = 0; double rt = 0, ra = 0; + FILE *ci = fopen("/proc/cpuinfo", "r"); + if (ci) { char ln[256]; + while (fgets(ln, sizeof(ln), ci)) if (!strncmp(ln, "model name", 10)) { + char *p = strchr(ln, ':'); if (p) { p++; while (*p == ' ') p++; + int n = (int)strlen(p); if (n > 0 && p[n-1] == '\n') p[--n] = 0; + snprintf(cpu, sizeof(cpu), "%s", p); } break; } + fclose(ci); } +#ifdef _SC_NPROCESSORS_ONLN + cores = (int)sysconf(_SC_NPROCESSORS_ONLN); +#endif + FILE *mi = fopen("/proc/meminfo", "r"); + if (mi) { char ln[256]; double v = 0; + while (fgets(ln, sizeof(ln), mi)) { + if (sscanf(ln, "MemTotal: %lf", &v) == 1) rt = v/1e6; + if (sscanf(ln, "MemAvailable: %lf", &v) == 1) ra = v/1e6; + } fclose(mi); } + int ngpu = 0; double vram = 0; + const char *gpu = ""; +#ifdef COLI_CUDA + if (g_cuda) { ngpu = 1; vram = ink_cuda_free_bytes()/1e9; gpu = "CUDA device"; } +#endif + (void)m; + printf("HWINFO %d %.1f %.1f %d %.1f %s|%s\n", cores, rt, ra, ngpu, vram, cpu[0]?cpu:"unknown", gpu); + fflush(stdout); +} + +static void serve_tiers_emap(Model *m) { + Cfg *c = &m->c; int E = c->n_experts; + int nsp = 0, filled = 0; + for (int i = 0; i < c->n_layers; i++) if (c->sparse[i]) { nsp++; filled += m->cache[i].n; } + int64_t I = c->moe_inter, D = c->hidden; + int64_t slotb = m->xq ? m->rb13*2*I + m->rb2*D + (2*I+D)*4 + : m->quant_bits ? 3*I*D + (2*I+D)*4 : 3*I*D*4; + printf("TIERS 0 %d %d 0.00 %.2f\n", filled, nsp*E - filled, filled*(double)slotb/1e9); + /* EMAP: 1 byte/expert hex — tier(2b: 0=disk 1=RAM)<<6 | heat(6b: log2 usage) */ + char *hex = malloc((size_t)nsp*E*2 + 1); int w = 0; + for (int i = 0; i < c->n_layers; i++) { + if (!c->sparse[i]) continue; + LCache *lc = &m->cache[i]; + for (int e = 0; e < E; e++) { + int tier = 0; + for (int z = 0; z < lc->n; z++) if (lc->slots[z].eid == e && lc->slots[z].filled) { tier = 1; break; } + uint32_t u = m->eusage[i] ? m->eusage[i][e] : 0; + int heat = 0; while (u) { heat++; u >>= 1; } if (heat > 63) heat = 63; + int b = (tier << 6) | heat; + hex[w++] = "0123456789abcdef"[b >> 4]; + hex[w++] = "0123456789abcdef"[b & 15]; + } + } + hex[w] = 0; + printf("EMAP %d %d %s\n", nsp, E, hex); + fflush(stdout); free(hex); +} + +static void serve_loop(Model *m, Tok *T) { + setvbuf(stdin, NULL, _IONBF, 0); + const char *sd = getenv("SEED"); + if (sd) g_rng ^= (uint64_t)strtoull(sd, NULL, 10); + else g_rng ^= (uint64_t)time(NULL) * 2654435761u; + /* the gateway reads a STAT line right after the READY sentinel (colibri + * reports its load stats there) — match the handshake */ + fputs("\x01\x01READY\x01\x01\n", stdout); + printf("STAT 0 0.0 0.0 %.2f 0 0\n", rss_gb()); + fflush(stdout); + serve_hwinfo(m); + serve_tiers_emap(m); + for (;;) { + while (!g_qn) if (serve_read_cmd(NULL) < 0) return; /* blocks on stdin */ + SReq q = g_q[0]; + memmove(g_q, g_q+1, (size_t)(--g_qn) * sizeof(SReq)); + serve_one(m, T, &q); + serve_tiers_emap(m); + free(q.payload); + } +} + /* ---------- ref_inkling.json harness ---------- */ static int *read_int_array(jval *o, const char *key, int *n_out) { jval *a = json_get(o, key); @@ -1178,6 +1412,18 @@ int main(int argc, char **argv) { if (cap < 0) cap = (prompt || pfile) ? 0 : 16; /* generate mode defaults to RAM-sized auto cap */ if (bits && (bits < 2 || bits > 8)) { fprintf(stderr, "quant_bits must be 0 (f32) or 2..8\n"); return 1; } + /* SERVE=1: the openai_server.py gateway drives the engine over stdin/stdout + * (READY handshake, SUBMIT/CANCEL, DATA/DONE frames) — same protocol colibri. */ + if (getenv("SERVE") && getenv("SERVE")[0] == '1') { + Model m; model_init(&m, snap, cap, bits); + pins_load(&m, snap); + char tkp[2048]; snprintf(tkp, sizeof(tkp), "%s/tokenizer.json", snap); + Tok T; tok_load(&T, tkp); + serve_loop(&m, &T); + usage_save(&m, snap); + return 0; + } + if (prompt || pfile) { Model m; model_init(&m, snap, cap, bits); printf("== Inkling C engine, %d layers, experts @ %s, cache %d/layer ==\n", From aa188fe55f1251be63b51d548f733bcbdc0cd59e Mon Sep 17 00:00:00 2001 From: Nicholas Beerbower Date: Wed, 22 Jul 2026 22:06:13 -0400 Subject: [PATCH 22/96] openai_server: serve the inkling engine (ARCH detect + reasoning channel) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit With inkling.c now speaking the serve protocol, teach the gateway to drive it. Additive — the glm path is byte-unchanged (ARCH defaults to "glm"): - --arch {auto,glm,inkling}; auto reads model_type from the model's config.json. Picks the chat-template renderer (render_chat_inkling) and the model-id. - inkling's thinking is reasoning, not answer: split the <|content_thinking|> / <|content_text|> channels (split_inkling / strip_inkling_markers) into reasoning_content on both the streaming (InklingStreamSplit) and non-streaming chat paths. - response_format grammars are refused for inkling (its serve loop speaks the 6-field SUBMIT header only) with a clear 400. 51 openai_server tests pass; end-to-end serve smoke against a tiny model returns DATA/DONE/PROF and coherent reasoning/text split. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_016KLDCYJSyHxd39ChTr4T27 --- c/openai_server.py | 193 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 187 insertions(+), 6 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 6b8632de..48840750 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -357,6 +357,8 @@ def parse_tool_calls(reply, tools=None): text = _BOX_RE.sub("", reply) if tail is not None: # drop the recovered tail from the visible content text = text[:text.rindex(BOX_START)] + if ARCH == "inkling": + text = strip_inkling_markers(text) # thinking is reasoning, not answer if THINK_CLOSE in text: text = text.split(THINK_CLOSE, 1)[1] text = text.replace(THINK_OPEN, "").replace(THINK_CLOSE, "") @@ -371,6 +373,150 @@ def parse_tool_calls(reply, tools=None): return text.strip(), calls +ARCH = "glm" # set in main(): "glm" | "inkling" (auto-detected from the model's config.json) + +INK_THINK, INK_TEXT = "<|content_thinking|>", "<|content_text|>" + + +class InklingStreamSplit: + """Strips Inkling's content markers from the visible stream and withholds + <|content_thinking|> sections from `content` (they are reasoning, not + answer). Buffers partial markers across chunk boundaries so a marker split + between two DATA frames never leaks.""" + + def __init__(self, on_content, on_reasoning=None): + self.on_content = on_content + self.on_reasoning = on_reasoning + self.mode = "content" + self.buf = "" + + def feed(self, piece): + self.buf += piece + while True: + hits = [(i, m) for i, m in ((self.buf.find(INK_THINK), INK_THINK), + (self.buf.find(INK_TEXT), INK_TEXT)) if i >= 0] + if not hits: + hold = self._tail_hold() + out = self.buf[:len(self.buf) - hold] if hold else self.buf + self.buf = self.buf[len(self.buf) - hold:] if hold else "" + self._emit(out) + return + i, m = min(hits) + self._emit(self.buf[:i]) + self.mode = "reasoning" if m == INK_THINK else "content" + self.buf = self.buf[i + len(m):] + + def _tail_hold(self): + for k in range(min(len(self.buf), 24), 0, -1): + if INK_THINK.startswith(self.buf[-k:]) or INK_TEXT.startswith(self.buf[-k:]): + return k + return 0 + + def _emit(self, text): + if not text: + return + text = _INK_MARKER.sub("", text) + if not text: + return + if self.mode == "content": + self.on_content(text) + elif self.on_reasoning: + self.on_reasoning(text) + + def close(self): + self._emit(self.buf) + self.buf = "" + + +import re as _re +_INK_MARKER = _re.compile(r"<\|(?:content_\w+|end_message|message_\w+|audio_end|unused_\d+)\|>") + +def strip_inkling_markers(text): + """Remove <|content_thinking|>…<|content_text|> sections, then any stray + control markers (end_message, role/content tokens) the model emits.""" + while INK_THINK in text: + pre, _, rest = text.partition(INK_THINK) + _, _, after = rest.partition(INK_TEXT) + text = pre + after + return _INK_MARKER.sub("", text) + + +def split_inkling(text): + """Split raw Inkling output into (content, reasoning). Thinking blocks + (<|content_thinking|>…<|content_text|>) become reasoning — including an + UNTERMINATED trailing block (budget ran out mid-thought), which partitions + to everything after the opener — so a think-only generation surfaces its + reasoning instead of collapsing to an empty answer.""" + reasoning = [] + while INK_THINK in text: + pre, _, rest = text.partition(INK_THINK) + think, _, after = rest.partition(INK_TEXT) + reasoning.append(think) + text = pre + after + return _INK_MARKER.sub("", text), _INK_MARKER.sub("", "".join(reasoning)) + + +def render_chat_inkling(messages, enable_thinking=False, reasoning_effort=None, tools=None, + tool_choice=None): + """Text-only subset of Inkling's chat_template.jinja: role tokens with + <|content_text|> parts and <|end_message|> terminators, an assistant + <|content_model_end_sampling|> after each prior model turn, the + thinking-effort hint appended after the messages (the template's fallback + branch), then <|message_model|> as the generation prompt.""" + if not isinstance(messages, list) or not messages: + raise APIError(400, "`messages` must be a non-empty array.", "messages") + if tools or (tool_choice not in (None, "none")): + raise APIError(400, "Tool use is not wired up for the Inkling engine yet.", + "tools", "unsupported_parameter") + role_token = {"user": "<|message_user|>", "system": "<|message_system|>", + "developer": "<|message_system|>", "assistant": "<|message_model|>", + "tool": "<|message_tool|>"} + # Thinking effort — template default is 0.9, but at single-machine decode + # speeds unrequested reasoning burns the whole token budget before the answer + # starts, so we default it OFF unless the client asks. + effort_map = {"none": 0.0, "minimal": 0.1, "low": 0.2, "medium": 0.7, + "high": 0.9, "max": 0.99} + if reasoning_effort in effort_map: + eff = effort_map[reasoning_effort] + else: + eff = 0.9 if enable_thinking else 0.0 + effort_str = ("<|message_system|><|content_text|>Thinking effort level: " + f"{0 if eff == 0.0 else eff}<|end_message|>") + + prompt = [] + effort_emitted = False + for index, message in enumerate(messages): + if not isinstance(message, dict): + raise APIError(400, "Each message must be an object.", f"messages.{index}") + role = message.get("role") + rtok = role_token.get(role) + if rtok is None: + raise APIError(400, f"Unsupported role {role!r}.", f"messages.{index}.role") + # the template emits the effort hint inline, right before the first + # non-system message — not at the end. Position matters: it changes the + # exact token sequence the model was trained on. + if not effort_emitted and role not in ("system", "developer"): + prompt.append(effort_str) + effort_emitted = True + raw = message.get("content") + text = content_text(raw, f"messages.{index}.content") if raw is not None else "" + prompt.append(f"{rtok}<|content_text|>{text}<|end_message|>") + if role == "assistant": + prompt.append("<|content_model_end_sampling|>") + if not effort_emitted: # all-system edge case: fallback + prompt.append(effort_str) + prompt.append("<|message_model|>") # add_generation_prompt + # Thinking off: prefill the content channel. Without this the model can still + # sample <|content_thinking|> as its first token (the effort hint is only a + # soft signal), open a reasoning block, and burn the whole token budget before + # reaching <|content_text|> — which the splitter then strips to an empty + # answer. Ending the prompt at <|message_model|><|content_text|> forces content + # mode; it is exactly the sequence every non-thinking turn is trained on. + if eff == 0.0: + prompt.append("<|content_text|>") + return "".join(prompt) + + def render_chat(messages, enable_thinking=False, reasoning_effort=None, tools=None, tool_choice=None): """Render the text-only subset of the official GLM-5.2 chat template.""" @@ -1222,6 +1368,11 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non sys.stderr.write(f"\n===== PROMPT [{request_id}] =====\n{prompt}\n===== OUTPUT [{request_id}] =====\n") sys.stderr.flush() maximum, temperature, top_p, grammar = generation_options(body, self.server.max_tokens) + if grammar is not None and ARCH == "inkling": + # inkling.c's serve loop speaks the 6-field SUBMIT header only; sending the + # grammar payload extension would desync its stdin framing. + raise APIError(400, "`response_format` grammars are not supported by the Inkling " + "engine yet.", "response_format", "unsupported_parameter") # tools and tool_choice come from chat_completion() already processed/filtered if chat and tool_choice == "none": tools = None # client forbade tools: never surface tool_calls @@ -1252,17 +1403,25 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non prompt, maximum, temperature, top_p, output.append, cache_slot, self.client_disconnected, grammar=grammar) text = "".join(output) + reasoning = "" + if ARCH == "inkling": + text, reasoning = split_inkling(text) length_finish = "length" if stats["length_limited"] else "stop" if chat and tools: content, calls = parse_tool_calls(text, tools) message = {"role": "assistant", "content": content or None, "refusal": None} + if reasoning: + message["reasoning_content"] = reasoning if calls: message["tool_calls"] = calls finish = "tool_calls" if calls else length_finish choice = {"index": 0, "message": message, "logprobs": None, "finish_reason": finish} else: - choice = ({"index": 0, "message": {"role": "assistant", "content": text, - "refusal": None}, "logprobs": None, "finish_reason": length_finish} if chat else + _msg = {"role": "assistant", "content": text, "refusal": None} + if reasoning: + _msg["reasoning_content"] = reasoning + choice = ({"index": 0, "message": _msg, + "logprobs": None, "finish_reason": length_finish} if chat else {"index": 0, "text": text, "logprobs": None, "finish_reason": length_finish}) self.send_json(200, {"id": completion_id, "object": object_name, "created": created, "model": self.server.model_id, "choices": [choice], "usage": self.usage(stats)}, @@ -1328,6 +1487,13 @@ def emit(text): {"index": 0, "text": text, "logprobs": None, "finish_reason": None}) event([choice]) + def emit_reasoning(text): # thinking → reasoning_content deltas (chat only) + event([{"index": 0, "delta": {"reasoning_content": text}, + "logprobs": None, "finish_reason": None}]) + + splitter = (InklingStreamSplit(emit, emit_reasoning if chat else None) + if ARCH == "inkling" else None) + ka_thread = threading.Thread(target=_keepalive, daemon=True) ka_thread.start() if chat and tools: @@ -1371,10 +1537,12 @@ def emit_tools(chunk): def emit_plain(chunk): if dbg_echo: sys.stderr.write(chunk); sys.stderr.flush() - emit(chunk) + (splitter.feed if splitter else emit)(chunk) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, emit_plain, cache_slot, lambda: not connected, grammar=grammar) + if splitter: + splitter.close() finish = "length" if stats["length_limited"] else "stop" ka_stop.set() # generation done: stop the keepalive pump ka_thread.join(timeout=2) @@ -1427,8 +1595,9 @@ def chat_completion(self, body, request_id): raise APIError(400, "`enable_thinking` must be a boolean.", "enable_thinking") tools = body.get("tools") or body.get("functions") or None tool_choice = body.get("tool_choice") - prompt = render_chat(body.get("messages"), enable_thinking, reasoning_effort, tools, - tool_choice) + renderer = render_chat_inkling if ARCH == "inkling" else render_chat + prompt = renderer(body.get("messages"), enable_thinking, reasoning_effort, tools, + tool_choice) self.generation(body, prompt, request_id, True, tools, tool_choice) # ---- Anthropic /v1/messages (#343) ---------------------------------------------------- @@ -1673,9 +1842,11 @@ def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--model", default=os.environ.get("COLI_MODEL"), required=not os.environ.get("COLI_MODEL")) parser.add_argument("--engine", default=str(default_engine())) + parser.add_argument("--arch", choices=("auto", "glm", "inkling"), default="auto", + help="chat-template family; auto reads model_type from the model's config.json") parser.add_argument("--host", default="127.0.0.1") parser.add_argument("--port", type=int, default=8000) - parser.add_argument("--model-id", default=os.environ.get("COLI_MODEL_ID", "glm-5.2-colibri")) + parser.add_argument("--model-id", default=os.environ.get("COLI_MODEL_ID")) parser.add_argument("--api-key", default=os.environ.get("COLI_API_KEY")) parser.add_argument("--cors-origin", action="append", default=None, help="allowed browser origin; repeat as needed (use '*' for any origin)") @@ -1686,6 +1857,16 @@ def main(): default=float(os.environ.get("COLI_QUEUE_TIMEOUT", "300"))) parser.add_argument("--kv-slots", type=int, default=int(os.environ.get("COLI_KV_SLOTS", "1"))) args = parser.parse_args() + global ARCH + ARCH = args.arch + if ARCH == "auto": + try: + with open(Path(args.model) / "config.json") as fh: + ARCH = "inkling" if "inkling" in (json.load(fh).get("model_type") or "") else "glm" + except OSError: + ARCH = "glm" + if args.model_id is None: + args.model_id = "inkling-colibri" if ARCH == "inkling" else "glm-5.2-colibri" serve(args.model, args.host, args.port, args.model_id, args.api_key, args.cap,args.max_tokens,args.engine,cors_origins=args.cors_origin, max_queue=args.max_queue,queue_timeout=args.queue_timeout,kv_slots=args.kv_slots) From 6a921772ba7eecaac37d65d5b35f4c1b8a1adc28 Mon Sep 17 00:00:00 2001 From: Hermes Quant Auditor Date: Thu, 23 Jul 2026 10:57:59 +0800 Subject: [PATCH 23/96] test: fix CUDA env test for colibri rename + CPU-only CI MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Update binary path from glm to colibri (binary was renamed on dev) - Rename _HAS_GLM → _HAS_BINARY, _run_glm → _run for clarity - Update all docstrings and comments referencing glm.c → colibri.c - All 3 CPU-only tests pass, 6 GPU tests skip cleanly - Fixes test_cuda_zero_with_gpu_env_fails_guard: already had correct assertions for both CUDA and CPU-only builds --- c/tests/test_cuda_env.py | 60 ++++++++++++++++++++-------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/c/tests/test_cuda_env.py b/c/tests/test_cuda_env.py index 3cfbf427..6c0b91b2 100644 --- a/c/tests/test_cuda_env.py +++ b/c/tests/test_cuda_env.py @@ -1,11 +1,11 @@ """Integration tests for COLI_CUDA auto-enable and CUDA_EXPERT_GB auto-size. -These tests run the compiled ``glm`` binary (via ``coli run``) and verify +These tests run the compiled ``colibri`` binary and verify the three COLI_CUDA modes (unset / auto-detect, 0 / forced-CPU, 1 / hard-fail) and the CUDA_EXPERT_GB auto-size behavior. Prerequisites (tests skip gracefully when unmet): -- Compiled ``coli`` / ``glm`` binary (any build — CUDA or CPU-only) +- Compiled ``colibri`` binary (any build — CUDA or CPU-only) - nvidia-smi (for GPU-specific tests) """ @@ -20,16 +20,16 @@ HERE = Path(__file__).resolve().parent.parent -GLM = HERE / ("glm.exe" if sys.platform == "win32" else "glm") +COLIBRI = HERE / ("colibri.exe" if sys.platform == "win32" else "colibri") def _binary_has_cuda(): - """Check whether the ``glm`` binary was compiled with CUDA support.""" - if not GLM.exists(): + """Check whether the ``colibri`` binary was compiled with CUDA support.""" + if not COLIBRI.exists(): return False try: result = subprocess.run( - [str(GLM), "1"], + [str(COLIBRI), "1"], env={**os.environ, "COLI_CUDA": "1", "SNAP": str(HERE)}, cwd=str(HERE), text=True, capture_output=True, timeout=10, @@ -51,8 +51,8 @@ def _gpu_available(): return False -_HAS_GLM = GLM.exists() -_HAS_CUDA_BINARY = _HAS_GLM and _binary_has_cuda() +_HAS_BINARY = COLIBRI.exists() +_HAS_CUDA_BINARY = _HAS_BINARY and _binary_has_cuda() _HAS_GPU = _gpu_available() @@ -101,13 +101,13 @@ def _minimal_model(parent): class CudaStartupTest(unittest.TestCase): - """COLI_CUDA mode tests — call ``glm`` directly to exercise ``main()`` - including the CUDA init block at c/glm.c.""" + """COLI_CUDA mode tests — call ``colibri`` directly to exercise ``main()`` + including the CUDA init block at c/colibri.c.""" @classmethod def setUpClass(cls): - if not _HAS_GLM: - raise unittest.SkipTest("glm binary not found") + if not _HAS_BINARY: + raise unittest.SkipTest("colibri binary not found") def setUp(self): self.tmp = tempfile.TemporaryDirectory() @@ -115,8 +115,8 @@ def setUp(self): def tearDown(self): self.tmp.cleanup() - def _run_glm(self, cap="1", env=None, timeout=30): - """Run the glm binary directly with a minimal model. + def _run(self, cap="1", env=None, timeout=30): + """Run the colibri binary directly with a minimal model. The binary will fail during model_init (fake weights) but CUDA init messages are emitted first. @@ -124,7 +124,7 @@ def _run_glm(self, cap="1", env=None, timeout=30): model = _minimal_model(self.tmp.name) merged = {**os.environ, "SNAP": str(model), **(env or {})} return subprocess.run( - [str(GLM), cap], + [str(COLIBRI), cap], env=merged, cwd=str(HERE), text=True, capture_output=True, timeout=timeout, ) @@ -133,20 +133,20 @@ def _run_glm(self, cap="1", env=None, timeout=30): def test_cuda_zero_suppresses_all_cuda_output(self): """COLI_CUDA=0 must not emit any [CUDA] line to stderr.""" - result = self._run_glm(env={"COLI_CUDA": "0"}) + result = self._run(env={"COLI_CUDA": "0"}) self.assertNotIn("[CUDA]", result.stderr or "") def test_cuda_zero_with_gpu_env_fails_guard(self): - """COLI_GPU with COLI_CUDA=0 exits non-zero (guard in glm.c). + """COLI_GPU with COLI_CUDA=0 exits non-zero (guard in colibri.c). On CPU-only binary the guard fires from the #else branch with a different message; both are valid and tested here. """ - result = self._run_glm(env={"COLI_CUDA": "0", "COLI_GPU": "0"}) + result = self._run(env={"COLI_CUDA": "0", "COLI_GPU": "0"}) self.assertNotEqual(result.returncode, 0) if _HAS_CUDA_BINARY: - # CUDA build: guard at glm.c L6424 — COLI_GPU(S) requires COLI_CUDA=1 + # CUDA build: guard at colibri.c — COLI_GPU(S) requires COLI_CUDA=1 self.assertIn("COLI_GPU(S) requires COLI_CUDA=1", result.stderr) else: # CPU-only build: #else branch — CUDA was requested, but CPU-only @@ -158,7 +158,7 @@ def test_cuda_one_cpu_only_binary_exits_with_rebuild_message(self): """CPU-only binary: COLI_CUDA=1 → exit≠0, 'CPU-only' in stderr.""" if _HAS_CUDA_BINARY: self.skipTest("binary has CUDA; CPU-only rejection not reachable") - result = self._run_glm(env={"COLI_CUDA": "1"}) + result = self._run(env={"COLI_CUDA": "1"}) self.assertNotEqual(result.returncode, 0) self.assertIn("CPU-only", result.stderr) @@ -168,7 +168,7 @@ def test_cuda_one_without_gpu_exits_two(self): self.skipTest("CPU-only binary; covered by cpu-only test") if _HAS_GPU: self.skipTest("GPU present; cannot simulate missing backend") - result = self._run_glm(env={"COLI_CUDA": "1"}) + result = self._run(env={"COLI_CUDA": "1"}) self.assertEqual(result.returncode, 2) self.assertIn("[CUDA] requested backend is unavailable", result.stderr) @@ -180,7 +180,7 @@ def test_cuda_unset_without_gpu_falls_back_to_cpu(self): self.skipTest("CPU-only binary has no auto-detect path") if _HAS_GPU: self.skipTest("GPU present; cannot simulate auto-detect fallback") - result = self._run_glm() + result = self._run() self.assertNotEqual(result.returncode, 2) self.assertIn("auto-detect: backend unavailable", result.stderr) @@ -190,7 +190,7 @@ def test_cuda_unset_with_gpu_enables_cuda(self): self.skipTest("CPU-only binary") if not _HAS_GPU: self.skipTest("no GPU available") - result = self._run_glm() + result = self._run() self.assertNotEqual(result.returncode, 2) self.assertIn("[CUDA] mode:", result.stderr) @@ -200,7 +200,7 @@ def test_cuda_expert_gb_zero_disables_auto_size(self): """Explicit CUDA_EXPERT_GB=0 — no auto-size message at startup.""" if not _HAS_CUDA_BINARY: self.skipTest("CPU-only binary") - result = self._run_glm(env={"CUDA_EXPERT_GB": "0"}) + result = self._run(env={"CUDA_EXPERT_GB": "0"}) self.assertNotIn("auto-sized", result.stderr or "") @@ -209,8 +209,8 @@ class CudaExpertBudgetTest(unittest.TestCase): @classmethod def setUpClass(cls): - if not _HAS_GLM: - raise unittest.SkipTest("glm binary not found") + if not _HAS_BINARY: + raise unittest.SkipTest("colibri binary not found") if not _HAS_CUDA_BINARY: raise unittest.SkipTest("CPU-only binary — auto-size path not compiled in") if not _HAS_GPU: @@ -222,25 +222,25 @@ def setUp(self): def tearDown(self): self.tmp.cleanup() - def _run_glm(self, cap="1", env=None, timeout=60): + def _run(self, cap="1", env=None, timeout=60): model = _minimal_model(self.tmp.name) merged = {**os.environ, "SNAP": str(model), **(env or {})} return subprocess.run( - [str(GLM), cap], + [str(COLIBRI), cap], env=merged, cwd=str(HERE), text=True, capture_output=True, timeout=timeout, ) def test_auto_size_prints_budget_when_expert_gb_unset(self): """CUDA enabled, CUDA_EXPERT_GB unset → 'auto-sized' in stderr.""" - result = self._run_glm(env={"COLI_CUDA": "1"}) + result = self._run(env={"COLI_CUDA": "1"}) combined = (result.stderr or "") + (result.stdout or "") if "auto-sized" in combined: self.assertIn("expert budget auto-sized", combined) def test_explicit_expert_gb_zero_suppresses_auto_size_at_pin_time(self): """CUDA_EXPERT_GB=0 → no auto-size message even at pin time.""" - result = self._run_glm(env={"COLI_CUDA": "1", "CUDA_EXPERT_GB": "0"}) + result = self._run(env={"COLI_CUDA": "1", "CUDA_EXPERT_GB": "0"}) combined = (result.stderr or "") + (result.stdout or "") self.assertNotIn("auto-sized", combined) From 2c4e9de9e09a66a29c8a7d4872506403d803f10f Mon Sep 17 00:00:00 2001 From: BE Date: Thu, 23 Jul 2026 11:28:39 +0530 Subject: [PATCH 24/96] olmoe: add an AVX2 int8 dot-product path to matmul_q (x86 had none) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The fast path in matmul_q was ARM NEON only — every x86 build silently fell back to a scalar, byte-at-a-time loop even on CPUs with AVX2 available. Adds an AVX2 counterpart: sign-extend both int8 operands to int16 (exact, no precision loss), multiply+horizontal-pair-sum via _mm256_madd_epi16, then reduce to a scalar. Pure integer arithmetic, so it's bit-for-bit identical to the scalar reference. Correctness (verified): IDOT=0 (scalar) and IDOT=1 (AVX2) produce the exact same 100/100 tokens on OLMoE-1B-7B-0125-Instruct, same prompt. SNAP= IDOT=0 ./olmoe 64 8 ref.json SNAP= IDOT=1 ./olmoe 64 8 ref.json Performance: measured on an 8-core AMD Zen (Lucienne) CPU-only box, same 100-token prompt, cache=64 (all experts resident). This machine is NOT an isolated benchmark box — it was running a full kube-apiserver/etcd/kubelet stack, clickhouse-server, and several Chrome renderers throughout (load average ~4.0 on 8 cores), and that background load dominates the signal: IDOT=0 (scalar): 1.90 / 2.37 / 2.73 / 2.84 tok/s (median 2.55, n=4) IDOT=1 (AVX2): 2.54 / 2.64 / 3.90 tok/s (median 2.64, n=3) ~4% difference at the median — within the run-to-run noise on this machine, not a confirmed speedup. Not claiming a performance win here; this patch's value is x86/ARM parity and verified correctness. Re-measure on a quieter or dedicated box for a real throughput number. --- c/olmoe.c | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/c/olmoe.c b/c/olmoe.c index d9460d40..ffeb803d 100644 --- a/c/olmoe.c +++ b/c/olmoe.c @@ -149,9 +149,30 @@ static inline int32_t dot_i8_16(const int8_t *a, const int8_t *b) { #endif return vaddvq_s32(acc); } +#define HAVE_FAST_DOT_I8 1 +#elif defined(__AVX2__) +#include +/* x86 counterpart of the NEON path above (was scalar-only here before — + * the only fast path was ARM, so x86 boxes silently used the scalar + * fallback even when AVX2 was available). + * Sign-extend both int8 vectors to int16 (exact, no precision loss) then + * madd+horizontal-sum in int32: pure integer arithmetic, so this is + * bit-for-bit identical to the scalar dot product, just vectorized. */ +static inline int32_t dot_i8_16(const int8_t *a, const int8_t *b) { + __m256i va16 = _mm256_cvtepi8_epi16(_mm_loadu_si128((const __m128i*)a)); + __m256i vb16 = _mm256_cvtepi8_epi16(_mm_loadu_si128((const __m128i*)b)); + __m256i prod = _mm256_madd_epi16(va16, vb16); /* 8 x int32, adjacent pairs summed */ + __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(prod), _mm256_extracti128_si256(prod, 1)); + __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); + __m128i sum64 = _mm_add_epi32(sum128, hi64); + __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + __m128i sum32 = _mm_add_epi32(sum64, hi32); + return _mm_cvtsi128_si32(sum32); +} +#define HAVE_FAST_DOT_I8 1 #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) +#if defined(HAVE_FAST_DOT_I8) static int idot = -1; if (idot < 0) { const char *e = getenv("IDOT"); idot = !(e && *e == '0'); } if (idot && I % 16 == 0 && I <= 4096) { From ee0b40d8711118b38f9ce59b1ab1d957fcc4e97f Mon Sep 17 00:00:00 2001 From: BE Date: Thu, 23 Jul 2026 11:33:48 +0530 Subject: [PATCH 25/96] tools: make convert_olmoe_merged.py disk/RAM-safe (streaming, resumable) The old --repo path called snapshot_download (pulls the whole checkpoint to local disk first) then loaded every shard fully into RAM via load_file before processing -- for OLMoE-1B-7B's ~14GB bf16 checkpoint, that's a real problem on a RAM-constrained machine (this was hit directly: 21GB total RAM, most already in use by other processes). Rewrites both paths to be lazy: - --model (local dir): opens shards with safetensors.safe_open and reads tensors on demand instead of safetensors.torch.load_file's eager full-shard load. - --repo: downloads and processes ONE source shard at a time via hf_hub_download, deleting it immediately after extraction -- peak extra disk usage is one shard, not the full checkpoint. An expert's three projections can land in different shards; incomplete triples are held in a small in-RAM dict until their last projection arrives, then merged, quantized, and flushed. - Resumable: existing output shards are scanned (header-only, via safe_open) and already-converted tensors are skipped on a rerun, so an interrupted conversion picks up where it left off instead of restarting. Same output format and CLI as before (--repo/--model/--out), no behavior change for anyone already using it successfully -- this only changes how much RAM/disk it needs while doing so. Verified end-to-end: converted allenai/OLMoE-1B-7B-0125-Instruct on a 21GB-RAM machine (previously not attempted with the old eager-load version), all 1024 experts (16 layers x 64) present and complete, olmoe.c loads and runs correctly against the result. --- c/tools/convert_olmoe_merged.py | 312 ++++++++++++++++++++++---------- 1 file changed, 216 insertions(+), 96 deletions(-) diff --git a/c/tools/convert_olmoe_merged.py b/c/tools/convert_olmoe_merged.py index 883b8df2..fe034ec3 100644 --- a/c/tools/convert_olmoe_merged.py +++ b/c/tools/convert_olmoe_merged.py @@ -4,142 +4,262 @@ Consolidates gate_proj, up_proj, and down_proj into a single merged tensor per expert. This allows olmoe.c to load an expert in a single disk read call instead of 3. +DISK/RAM-SAFE: with --repo, source shards are downloaded and processed ONE AT A +TIME (deleted immediately after extraction) instead of pulling the whole +checkpoint via snapshot_download and loading every shard fully into RAM at +once. Peak extra disk usage is one source shard, not the full checkpoint; +peak extra RAM is bounded by in-flight partial experts (an expert whose three +projections happen to land in different shards), not the whole state dict. +Resumable: existing output shards are scanned (header only) and already-done +tensors are skipped on a rerun. + Usage: python tools/convert_olmoe_merged.py --repo allenai/OLMoE-1B-7B-0125-Instruct --out ./olmoe_merged + python tools/convert_olmoe_merged.py --model ./OLMoE-1B-7B-0125-Instruct --out ./olmoe_merged """ -import argparse, json, os, sys, re +import argparse +import re +import shutil +import sys from pathlib import Path -# Windows: force UTF-8 output -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 safetensors.torch import load_file, save_file - import huggingface_hub + from safetensors import safe_open + from safetensors.torch import save_file except ImportError as exc: sys.exit(f"Missing dependencies: {exc}. Install: pip install torch safetensors huggingface_hub") -EXPERT_KEY_RE = r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight" +EXPERT_KEY_RE = re.compile( + r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight" +) +SKIP_KEY_RE = re.compile(r"\.e_score_correction_bias$|^model\.rotary_emb\.") + +TOKENIZER_FILES = ( + "tokenizer.json", "tokenizer_config.json", "special_tokens_map.json", + "vocab.json", "merges.txt", "generation_config.json", +) -def quantize_row(w: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: - """Row-wise int8 quantization. Returns (int8_weights, float32_scales).""" + +def quantize_row(w: "torch.Tensor") -> tuple: + """Row-wise int8 quantization, identical math to olmoe.c's quantize_rows().""" w_f32 = w.float() row_max = w_f32.abs().amax(dim=1, keepdim=True).clamp(min=1e-12) scales = row_max / 127.0 q = (w_f32 / scales).round().clamp(-128, 127).to(torch.int8) return q, scales.squeeze(1) -def main(): - ap = argparse.ArgumentParser(description="Convert OLMoE HF checkpoint -> colibri merged int8") - src = ap.add_mutually_exclusive_group(required=True) - src.add_argument("--repo", help="HuggingFace repo ID") - src.add_argument("--model", help="Local HF checkpoint directory") - ap.add_argument("--out", required=True, help="Output directory for merged model") - args = ap.parse_args() - if args.repo: - from huggingface_hub import snapshot_download - from huggingface_hub.errors import LocalEntryNotFoundError - print(f"Downloading/Resolving {args.repo}...") - try: - src_dir = snapshot_download(args.repo, local_files_only=True, max_workers=4) - except LocalEntryNotFoundError: - src_dir = None - if src_dir is None or not any(Path(src_dir).glob("*.safetensors")): - print("Downloading safetensors...") - src_dir = snapshot_download(args.repo, max_workers=4) - else: - src_dir = args.model +def free_gb(path) -> float: + return shutil.disk_usage(path).free / 1e9 + + +def scan_existing_output(out_dir: Path) -> set: + """Tensor names already written to a *.safetensors file in out_dir -- + header-only scan, cheap even against many GB of already-converted shards.""" + done = set() + for shard in sorted(out_dir.glob("model-*.safetensors")): + with safe_open(str(shard), framework="pt") as f: + done.update(f.keys()) + return done + + +class OutputWriter: + """Buffers converted tensors and flushes into new model-NNNNN.safetensors + shard files once the buffer gets large -- st.h indexes every *.safetensors + file in a directory by scanning each one's own header, so any number of + arbitrarily-named shards works with no index.json needed.""" + + def __init__(self, out_dir: Path, flush_every: int = 512): + self.out_dir = out_dir + self.flush_every = flush_every + self.buf = {} + self.shard_idx = len(list(out_dir.glob("model-*.safetensors"))) + + def add(self, name: str, tensor: "torch.Tensor"): + self.buf[name] = tensor.contiguous() + if len(self.buf) >= self.flush_every: + self.flush() + + def flush(self): + if not self.buf: + return + fn = self.out_dir / f"model-{self.shard_idx:05d}.safetensors" + save_file(self.buf, str(fn)) + print(f" wrote {fn.name} ({len(self.buf)} tensors)", flush=True) + self.buf = {} + self.shard_idx += 1 + + +def convert_expert(gate, up, down): + q_gate, s_gate = quantize_row(gate) + q_up, s_up = quantize_row(up) + q_down, s_down = quantize_row(down) + merged_q = torch.cat([q_gate.flatten(), q_up.flatten(), q_down.flatten()]) + merged_s = torch.cat([s_gate, s_up, s_down]) + return merged_q, merged_s - src = Path(src_dir) - if not src.is_dir(): - sys.exit(f"Model directory not found: {src}") - if not (src / "config.json").is_file(): - sys.exit(f"config.json missing in {src}") - out = Path(args.out) - out.mkdir(parents=True, exist_ok=True) +def copy_metadata(src_dir: Path, out_dir: Path): + shutil.copy2(src_dir / "config.json", out_dir / "config.json") + for fn in TOKENIZER_FILES: + p = src_dir / fn + if p.is_file(): + shutil.copy2(p, out_dir / fn) - # Copy config.json - import shutil - shutil.copy2(src / "config.json", out / "config.json") - print(f"config.json -> {out}") - # Process safetensors - shards = sorted(src.glob("*.safetensors")) +# ---------- local / pre-downloaded path ---------- + +def convert_local(src_dir: Path, out_dir: Path, flush_every: int): + copy_metadata(src_dir, out_dir) + shards = sorted(src_dir.glob("*.safetensors")) if not shards: - sys.exit(f"No safetensors found in {src}") + sys.exit(f"No safetensors found in {src_dir}") + + name_to_shard = {} + for shard in shards: + with safe_open(str(shard), framework="pt") as f: + for name in f.keys(): + name_to_shard[name] = shard + + done = scan_existing_output(out_dir) + writer = OutputWriter(out_dir, flush_every) + handles = {} - print("Loading all shards to build complete state dict...") - state_dict = {} - for si, shard in enumerate(shards, 1): - print(f"Loading shard {si}/{len(shards)}: {shard.name}...") - tensors = load_file(str(shard)) - state_dict.update(tensors) + def get(name): + shard = name_to_shard[name] + h = handles.get(shard) + if h is None: + h = safe_open(str(shard), framework="pt") + handles[shard] = h + return h.get_tensor(name) - # Gather experts experts = {} - for name in list(state_dict.keys()): - m = re.match(EXPERT_KEY_RE, name) + dense_names = [] + for name in name_to_shard: + if SKIP_KEY_RE.search(name): + continue + m = EXPERT_KEY_RE.match(name) if m: - layer_idx, expert_idx, proj = m.groups() - layer_idx = int(layer_idx) - expert_idx = int(expert_idx) - key = (layer_idx, expert_idx) - if key not in experts: - experts[key] = {} - experts[key][proj] = state_dict.pop(name) + layer, expert, proj = int(m.group(1)), int(m.group(2)), m.group(3) + experts.setdefault((layer, expert), {})[proj] = name + else: + dense_names.append(name) - print(f"Found {len(experts)} experts to merge.") - - # Process and merge experts - out_tensors = {} - total_expert_f32 = 0 - total_expert_q = 0 + for name in dense_names: + if name in done: + continue + writer.add(name, get(name)) + writer.flush() + n_total = len(experts) + n_done = 0 for (layer, expert), projs in sorted(experts.items()): - if not ("gate_proj" in projs and "up_proj" in projs and "down_proj" in projs): - sys.exit(f"Missing projection for layer {layer} expert {expert}!") + mw = f"model.layers.{layer}.mlp.experts.{expert}.merged_weight" + if mw in done: + n_done += 1 + continue + if not all(k in projs for k in ("gate_proj", "up_proj", "down_proj")): + sys.exit(f"Missing projection for layer {layer} expert {expert}: have {list(projs)}") + gate, up, down = get(projs["gate_proj"]), get(projs["up_proj"]), get(projs["down_proj"]) + merged_q, merged_s = convert_expert(gate, up, down) + writer.add(mw, merged_q) + writer.add(f"model.layers.{layer}.mlp.experts.{expert}.qs", merged_s) + n_done += 1 + if n_done % 100 == 0 or n_done == n_total: + print(f" {n_done}/{n_total} experts converted...", flush=True) + writer.flush() + print(f"Done: {n_total} experts, {len(dense_names)} dense tensors converted.") + - gate = projs["gate_proj"] - up = projs["up_proj"] - down = projs["down_proj"] +# ---------- streaming --repo path: one source shard downloaded/converted/deleted at a time ---------- - total_expert_f32 += (gate.numel() + up.numel() + down.numel()) * gate.element_size() +def convert_streaming(repo: str, out_dir: Path, flush_every: int, min_free_gb: float): + from huggingface_hub import HfApi, hf_hub_download - # Quantize each projection separately - q_gate, s_gate = quantize_row(gate) - q_up, s_up = quantize_row(up) - q_down, s_down = quantize_row(down) + info = HfApi().repo_info(repo, files_metadata=True) + shard_files = sorted(s.rfilename for s in info.siblings if s.rfilename.endswith(".safetensors")) + if not shard_files: + sys.exit(f"No .safetensors shards found in {repo}") - # Merge weights and scales contiguously - merged_q = torch.cat([q_gate.flatten(), q_up.flatten(), q_down.flatten()]) - merged_scales = torch.cat([s_gate, s_up, s_down]) + for fn in ("config.json",) + TOKENIZER_FILES: + try: + p = hf_hub_download(repo, fn, local_dir=str(out_dir) + "/_meta") + shutil.copy2(p, out_dir / fn) + except Exception: + pass + if not (out_dir / "config.json").is_file(): + sys.exit(f"config.json could not be fetched from {repo}") + + done = scan_existing_output(out_dir) + writer = OutputWriter(out_dir, flush_every) + pending_experts = {} # (layer, expert) -> {proj: tensor}, freed once a triple completes + + tmp = out_dir / "_inflight" + tmp.mkdir(exist_ok=True) - total_expert_q += merged_q.numel() * 1 + merged_scales.numel() * 4 + for si, shard_name in enumerate(shard_files): + if free_gb(out_dir) < min_free_gb: + print(f"STOP: free space below {min_free_gb} GB. Free space and rerun to resume.") + break + print(f"[{si+1}/{len(shard_files)}] downloading {shard_name} " + f"({free_gb(out_dir):.0f} GB free)...", flush=True) + local_path = hf_hub_download(repo, shard_name, local_dir=str(tmp)) - # Save to output - out_tensors[f"model.layers.{layer}.mlp.experts.{expert}.merged_weight"] = merged_q - out_tensors[f"model.layers.{layer}.mlp.experts.{expert}.qs"] = merged_scales + with safe_open(local_path, framework="pt") as f: + for name in f.keys(): + if SKIP_KEY_RE.search(name) or name in done: + continue + m = EXPERT_KEY_RE.match(name) + if not m: + writer.add(name, f.get_tensor(name)) + continue + layer, expert, proj = int(m.group(1)), int(m.group(2)), m.group(3) + key = (layer, expert) + mw = f"model.layers.{layer}.mlp.experts.{expert}.merged_weight" + if mw in done: + continue + slot = pending_experts.setdefault(key, {}) + slot[proj] = f.get_tensor(name) + if all(k in slot for k in ("gate_proj", "up_proj", "down_proj")): + merged_q, merged_s = convert_expert(slot["gate_proj"], slot["up_proj"], slot["down_proj"]) + writer.add(mw, merged_q) + writer.add(f"model.layers.{layer}.mlp.experts.{expert}.qs", merged_s) + del pending_experts[key] - # Copy remaining dense tensors - print(f"Adding remaining {len(state_dict)} dense tensors...") - out_tensors.update(state_dict) + Path(local_path).unlink(missing_ok=True) # delete the source shard now -- disk-safe + print(f" -> extracted+deleted {shard_name} " + f"({len(pending_experts)} experts still incomplete)", flush=True) + + writer.flush() + shutil.rmtree(tmp, ignore_errors=True) + if pending_experts: + print(f"WARNING: {len(pending_experts)} experts never completed all 3 projections -- rerun to resume.") + else: + print("DONE.") - # Save to a single output safetensors file for simpler loading - out_file = out / "model.safetensors" - print(f"Saving merged safetensors model to {out_file}...") - save_file(out_tensors, str(out_file)) - ratio = total_expert_q / max(total_expert_f32, 1) * 100 - print(f"\nDone. {len(experts)} experts successfully merged and saved.") - print(f"Expert storage: {total_expert_f32/1e9:.1f} GB -> {total_expert_q/1e9:.1f} GB ({ratio:.0f}%)") - print(f"Model ready at: {out}") +def main(): + ap = argparse.ArgumentParser(description="Convert OLMoE HF checkpoint -> colibri merged int8") + src = ap.add_mutually_exclusive_group(required=True) + src.add_argument("--repo", help="HuggingFace repo ID (streams+deletes one shard at a time)") + src.add_argument("--model", help="Local HF checkpoint directory (already fully downloaded)") + ap.add_argument("--out", required=True, help="Output directory for merged model") + ap.add_argument("--flush-every", type=int, default=512, + help="flush an output shard every N converted tensors") + ap.add_argument("--min-free-gb", type=float, default=5.0, + help="--repo only: stop (resumably) if free space drops below this") + args = ap.parse_args() + + out_dir = Path(args.out) + out_dir.mkdir(parents=True, exist_ok=True) + + if args.model: + convert_local(Path(args.model), out_dir, args.flush_every) + else: + convert_streaming(args.repo, out_dir, args.flush_every, args.min_free_gb) + if __name__ == "__main__": main() From 8b3f06c2346586456294384bd5b920c15b7708e1 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Thu, 23 Jul 2026 15:20:36 +0800 Subject: [PATCH 26/96] =?UTF-8?q?serve:=20cross-slot=20KV=20prefix=20adopt?= =?UTF-8?q?ion=20(COLI=5FKV=5FSHARE=3D1)=20=E2=80=94=20shared-prefix=20TTF?= =?UTF-8?q?T=2050.1s=20->=201.7s?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- c/colibri.c | 45 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..9bcc948c 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -5372,6 +5372,51 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g int prefix=0; while(prefixlen && prefixhist[prefix]==tmp[prefix]) prefix++; if(prefixlen){ sc->len=prefix; if(m->has_mtp) m->kv_start[m->c.n_layers]=-1; kv_disk_truncate(m,sc->len); } + /* Cross-slot prefix adoption (COLI_KV_SHARE=1) — RadixAttention's benefit + * at memcpy cost: if another slot's history shares a longer prefix with + * this prompt (shared system prompt, agent loop), copy its KV rows instead + * of recomputing them. MLA rows are position-addressed and self-contained + * (the same per-position serialization .coli_kv relies on), so a raw row + * copy is exact — the adopted prefill is skipped, not approximated. + * Copying beats aliasing here: step_decode_batch rejects two rows sharing + * one KVState, and a copy keeps every slot's lifetime independent. + * Measured (6x5090, 675-token shared prefix): slot TTFT 50.1s -> 1.7s, + * generated tokens identical to the full-prefill run. */ + static int kvshare=-1; + if(kvshare<0) kvshare=getenv("COLI_KV_SHARE")?atoi(getenv("COLI_KV_SHARE")):0; + if(kvshare && nctx>1 && prefix>=sc->len){ + int best=-1, blen=sc->len; + for(int i=0;ilenlen:nt, p=0; + while(phist[p]==tmp[p]) p++; + if(p>blen){ blen=p; best=i; } + } + if(best>=0){ + ServeCtx *dn=&ctx[best]; Cfg *cc=&m->c; int NR=cc->n_layers+1; + int from=sc->len, n=blen-from; + for(int l=0;lkv.Lc[l]&&dn->kv.Lc[l]) + memcpy(coli_kv_row(sc->kv.Lc[l],from,cc->kv_lora), + coli_kv_row(dn->kv.Lc[l],from,cc->kv_lora), + (size_t)n*cc->kv_lora*sizeof(float)); + if(sc->kv.Rc[l]&&dn->kv.Rc[l]) + memcpy(coli_kv_row(sc->kv.Rc[l],from,cc->qk_rope), + coli_kv_row(dn->kv.Rc[l],from,cc->qk_rope), + (size_t)n*cc->qk_rope*sizeof(float)); + if(sc->kv.Ic&&dn->kv.Ic&&sc->kv.Ic[l]&&dn->kv.Ic[l]) + memcpy(coli_kv_row(sc->kv.Ic[l],from,cc->index_hd), + coli_kv_row(dn->kv.Ic[l],from,cc->index_hd), + (size_t)n*cc->index_hd*sizeof(float)); + } + memcpy(sc->hist+from,tmp+from,(size_t)n*sizeof(int)); + sc->len=blen; + if(m->has_mtp) m->kv_start[cc->n_layers]=-1; /* MTP rows are per-slot decode state */ + fprintf(stderr,"[API] KV cross-slot adopt: slot %d took rows [%d,%d) from slot %d\n", + sub.slot,from,blen,best); + } + } int add=nt-sc->len; if(add>0) memcpy(sc->hist+sc->len,tmp+sc->len,(size_t)add*sizeof(int)); fprintf(stderr,"[API] KV slot %d prefix %d/%d token, prefill %d\n",sub.slot,sc->len,nt,add); From 8c00e0e9977dcaa9d3c2e5fd7fa856bb2d412252 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Thu, 23 Jul 2026 15:21:39 +0800 Subject: [PATCH 27/96] =?UTF-8?q?prefill:=20COLI=5FPREFILL=5FCHUNK=3DN=20?= =?UTF-8?q?=E2=80=94=20slice=20long=20prompts,=20bound=20activation=20memo?= =?UTF-8?q?ry=20to=20chunk=20size?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- c/colibri.c | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..51ae6427 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -4260,6 +4260,29 @@ static void kv_bind(Model *m, KVState *k){ static void mtp_absorb(Model *m, const int *next_ids, const float *x, int S, int pos_base); static float *step(Model *m, const int *ids, int S, int pos_base){ Cfg *c=&m->c; int D=c->hidden; + /* Chunked prefill (COLI_PREFILL_CHUNK=N): run a long prompt through the + * layers in N-token slices. KV rows are position-addressed, so a slice at + * pos_base+done is exactly the serve path's incremental suffix prefill — + * every S-scaled activation buffer here and inside attention/moe shrinks + * from prompt-sized to chunk-sized, and a serve scheduler gains a natural + * yield point between slices. Leading slices only write KV; logits come + * from the final slice's fall-through. Skipped under an active MTP draft: + * mtp_absorb consumes cross-position pairs a chunk boundary would split. + * Verified: 1,683-token prompt at N=256 produces byte-identical greedy + * output to the single-shot path. */ + static int chunk=-1; + if(chunk<0) chunk=getenv("COLI_PREFILL_CHUNK")?atoi(getenv("COLI_PREFILL_CHUNK")):0; + if(chunk>0 && S>chunk && !(m->has_mtp && g_draft>0)){ + int done=0; + while(S-done>chunk){ + float *cx=falloc((int64_t)chunk*D); + for(int s=0;s Date: Thu, 23 Jul 2026 15:24:04 +0800 Subject: [PATCH 28/96] =?UTF-8?q?cuda:=20fuse=20silu=20into=20the=20dual?= =?UTF-8?q?=20hidden=20kernels'=20epilogue=20=E2=80=94=20one=20less=20laun?= =?UTF-8?q?ch=20and=20no=20up[]=20round-trip=20per=20group=20call?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- c/backend_cuda.cu | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/c/backend_cuda.cu b/c/backend_cuda.cu index 1d32ff3c..71355c25 100644 --- a/c/backend_cuda.cu +++ b/c/backend_cuda.cu @@ -319,7 +319,13 @@ __global__ static void grouped_hidden_w4_dual(float *gate,float *up,const float int i=b*2;ga+=xs[i]*g0;ua+=xs[i]*u0;if(i+1>=1){if(threadIdx.x>=1){if(threadIdx.x=d.rows)return; @@ -879,18 +889,17 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates, dim3 hg((unsigned)I,(unsigned)max_rows,(unsigned)count),og((unsigned)D,(unsigned)max_rows,(unsigned)count); int dual=!getenv("COLI_CUDA_DUAL_PROJ")||atoi(getenv("COLI_CUDA_DUAL_PROJ")); if(dual)grouped_hidden_w4_dual<<stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D); - else{ + else{ /* non-dual path has no fused epilogue: silu stays a kernel here */ grouped_hidden_w4<<stream>>>(ctx->gate,ctx->x,dev,I,D,0); grouped_hidden_w4<<stream>>>(ctx->up,ctx->x,dev,I,D,1); + silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)total*I); } - silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)total*I); grouped_down_w4<<stream>>>(ctx->y,ctx->gate,dev,D,I); }else if(all_q4&&any_g4){ /* grouped-int4 (fmt=4) present: per-group scales (#334). fmt=2 members - * ride along as the ng=1 special case. */ + * ride along as the ng=1 special case. silu fused in the dual epilogue. */ dim3 hg((unsigned)I,(unsigned)max_rows,(unsigned)count),og((unsigned)D,(unsigned)max_rows,(unsigned)count); grouped_hidden_g4_dual<<stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D); - silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)total*I); grouped_down_g4<<stream>>>(ctx->y,ctx->gate,dev,D,I); }else{ /* generic path decodes fmt 0/1/2/3 only — a fmt=4 group that slipped the @@ -1438,8 +1447,7 @@ extern "C" int coli_cuda_expert_group_resident_issue(ColiCudaTensor *const *gate bcast_row<<<64,256,0,ctx->stream>>>(ctx->x,ctx->x,count,D); /* row 0 -> rows 1..count-1 (in-place safe: row 0 rewritten with itself) */ GroupDesc *dev=(GroupDesc*)ctx->group_desc; dim3 hg((unsigned)I,1,(unsigned)count),og((unsigned)D,1,(unsigned)count); - grouped_hidden_w4_dual<<stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D); - silu_mul<<<(unsigned)(((size_t)count*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)count*I); + grouped_hidden_w4_dual<<stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D); /* silu fused in epilogue */ grouped_down_w4<<stream>>>(ctx->y,ctx->gate,dev,D,I); weighted_sum_rows<<<48,256,0,ctx->stream>>>(partial_local,ctx->y,w_dev,count,D); if(!cuda_ok(cudaMemcpyPeerAsync(partial_slot_dev,home_device,partial_local,device, From 56b2ef965834d0a1333d45e1a45cda94f5f92932 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Thu, 23 Jul 2026 15:25:15 +0800 Subject: [PATCH 29/96] cuda: DSA sparse decode on GPU via gather staging (COLI_DSA_GATHER=1) --- c/colibri.c | 30 +++++++++++++++++++++++++++++- 1 file changed, 29 insertions(+), 1 deletion(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..83473bc0 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -2569,7 +2569,35 @@ static void attention_rows(Model *m, Layer *l, int layer, float *x, int S, int p for(int s=0;skv;int pos=positions?positions[s]:pos_base+s; int st0=ks->kv_start[layer],nt=pos+1-st0; - if(dnsel&&dnsel[s]>0){cuda_core=0;break;} + if(dnsel&&dnsel[s]>0){ + /* Sparse decode used to force the CPU path here: the CUDA + * absorb kernel only scans contiguous rows and cannot take + * the DSA gather list. COLI_DSA_GATHER=1: gather the + * selected top-k rows into a compact staging pair and hand + * the dense kernel that instead. Host KV stays canonical — + * only ~index_topk rows cross PCIe per (layer, token), so + * the device never needs the full context resident (the + * first stone of KV tiering). Same row set as the CPU loop + * below; results differ only by kernel-family FP order, + * the same class of divergence the dense CUDA absorb + * already has vs the CPU path (#510). Verified: with + * DSA_FORCE=1 (identity selection) output is byte-identical + * to the dense CUDA path. */ + static int dsag=-1; + if(dsag<0) dsag=getenv("COLI_DSA_GATHER")?atoi(getenv("COLI_DSA_GATHER")):0; + if(!dsag){cuda_core=0;break;} + int ns=dnsel[s]; const int *tl=dsel+(int64_t)s*dtopk; + float *gl=falloc((int64_t)ns*kvl), *gr=falloc((int64_t)ns*c->qk_rope); + for(int jj=0;jjLc[layer],tl[jj],kvl), (size_t)kvl*sizeof(float)); + memcpy(gr+(int64_t)jj*c->qk_rope, coli_kv_row(ks->Rc[layer],tl[jj],c->qk_rope), (size_t)c->qk_rope*sizeof(float)); + } + cuda_core=coli_cuda_attention_absorb(l->kv_b.cuda,ctx+(int64_t)s*H*vh, + Q+(int64_t)s*H*qh,gl,gr,H,c->qk_nope,c->qk_rope,vh,kvl,ns,c->attn_scale); + free(gl);free(gr); + if(!cuda_core)break; /* CPU fallback recomputes every row: idempotent */ + continue; + } cuda_core=0; if(g_cuda_pipe&&ks==m->kv&&layern_layers&&kv_dev_sync(m,l,layer,pos+1)) cuda_core=coli_cuda_attention_absorb_kvdev(l->kv_b.cuda,ctx+(int64_t)s*H*vh, From a7c859c0b298bac78079191d729c2e1e07102adb Mon Sep 17 00:00:00 2001 From: recviking Date: Thu, 23 Jul 2026 09:05:51 -0400 Subject: [PATCH 30/96] MTP: auto draft depth 1, not 3 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The best draft depth is platform- and workload-dependent: on CUDA a verify batch row is nearly free (S=2 ~ S=1 cost), so deeper drafts can pay on predictable text; on CPU each draft token costs a full extra token of expert compute; when disk-streaming, rejected drafts additionally fault experts that evict the LRU working set. Auto cannot know the workload — it should pick the depth that is safest everywhere, and that is 1, not 3: - single-token speculation had the highest acceptance in a GLM-5.2 744B sweep (~85% at depth 1 vs ~44-62% at 2-3): one draft token is a far safer bet than a run of them, still yielding ~1.9 accepted tokens/forward; - depth 1 was never the loser in any measured configuration (resident CUDA/ CPU, streaming Metal/CPU), while 3 was never the winner; - the downside of over-drafting is paid on every forward; the upside of deeper drafts only materializes on high-acceptance workloads — which users who have them can (and do) select explicitly. Explicit DRAFT=n still forces any depth; only the -1 auto default changes. Co-Authored-By: Claude Fable 5 --- c/colibri.c | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..391a1880 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -6558,9 +6558,15 @@ int main(int argc, char **argv){ * acceptance can still reach 30-50% even with the cold-expert mismatch. * See #292 for the diagnostic sweep that identified this. */ int cuda_mtp = getenv("COLI_CUDA_MTP") ? atoi(getenv("COLI_CUDA_MTP")) : 0; - g_draft = (m.has_mtp && (!g_cuda_enabled || cuda_mtp)) ? 3 : 0; + /* Auto depth = 1, not 3. A GLM-5.2 744B sweep (DRAFT=0/1/2/3, streaming and + * fully-resident) showed single-token speculation is the only depth that pays: + * acceptance ~85% at depth 1 vs ~44-62% at 2-3, and every extra draft token + * both costs verify compute and (when streaming) faults experts that evict the + * LRU working set. Depth 1 was the fastest MTP setting in every measured + * configuration; 2-3 never beat it anywhere. DRAFT=n still forces any depth. */ + g_draft = (m.has_mtp && (!g_cuda_enabled || cuda_mtp)) ? 1 : 0; #else - g_draft = m.has_mtp ? 3 : 0; + g_draft = m.has_mtp ? 1 : 0; #endif } if(getenv("DSA_TOPK")) m.c.index_topk=atoi(getenv("DSA_TOPK")); /* override per test */ From db5b46624b8892fcaa32fe135c514ebbb8d8d8ef Mon Sep 17 00:00:00 2001 From: bill Date: Thu, 23 Jul 2026 10:09:32 -0400 Subject: [PATCH 31/96] perf: balance causal attention prefill work --- c/colibri.c | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 9b4e4445..5faf3b48 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -2579,7 +2579,8 @@ static void attention_rows(Model *m, Layer *l, int layer, float *x, int S, int p } #endif if(!cuda_core){ - #pragma omp parallel for collapse(2) schedule(static) + /* Causal rows grow with s; round-robin pairs keep that work balanced. */ + #pragma omp parallel for collapse(2) schedule(static,1) for(int s=0;skv; int pos=positions?positions[s]:pos_base+s; @@ -2662,7 +2663,8 @@ static void attention_rows(Model *m, Layer *l, int layer, float *x, int S, int p int64_t sc_cap = Tk - stL; float *sc_all = falloc((int64_t)omp_get_max_threads()*sc_cap); double tac=now_s(); - #pragma omp parallel for collapse(2) schedule(static) + /* Causal rows grow with s; round-robin pairs keep that work balanced. */ + #pragma omp parallel for collapse(2) schedule(static,1) for(int s=0;s Date: Fri, 24 Jul 2026 00:25:20 +0800 Subject: [PATCH 32/96] pipe2: download the resident residual before every exit from residency MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two error paths in layers_forward_rows() cleared or bypassed PIPE2 device residency without bringing the residual back to host x. Since x host is STALE while x_dev_on>=0 (the block's own invariant), the CPU fallback for that layer then computed from a stale hidden state and every subsequent layer inherited it — wrong tokens, no error, no crash. - coli_cuda_pipe_scratch() returning NULL skipped the whole if(dst) body with no else, leaving x_dev_on>=0 and falling through to the CPU path. - A failed upload/peer_copy set x_dev_on=-1 while the authoritative copy was still on the PREVIOUS device, never downloaded. Both now download from x_dev_on before dropping residency. The upload failure case (x_dev_on<0) is unchanged: host x is already authoritative there. Fixes #557 --- c/colibri.c | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..459b8cfc 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -4176,7 +4176,13 @@ static void layers_forward_rows(Model *m, float *x, int S, int pos_base, l->kv_a.cuda_device==l->kv_b.cuda_device&&l->o.cuda_device==l->kv_b.cuda_device){ int dev=l->kv_b.cuda_device, ok=1; float *dst=coli_cuda_pipe_scratch(dev,15,xb); - if(dst){ + /* Ogni uscita dalla residenza deve riportare il residuo su host PRIMA di + * lasciare il layer al percorso CPU: x host e' STALE finche' x_dev_on>=0, + * quindi scaricare e' l'unico modo di non calcolare su uno stato vecchio. */ + if(!dst){ + /* niente scratch su questo device: si cade sul percorso CPU qui sotto */ + if(x_dev_on>=0){ coli_cuda_pipe_download(x_dev_on,x_dev,x,xb); x_dev_on=-1; } + } else { if(x_dev_on<0) ok=coli_cuda_pipe_upload(dev,dst,x,xb); else if(x_dev_on!=dev){ double tp=g_prof?now_s():0; @@ -4191,7 +4197,14 @@ static void layers_forward_rows(Model *m, float *x, int S, int pos_base, coli_cuda_pipe_peer_copy(dev,x_dev,dev,coli_cuda_pipe_scratch(dev,14,xb),xb); coli_cuda_pipe_download(dev,x_dev,x,xb); x_dev_on=-1; - }else x_dev_on=-1; + }else{ + /* upload o peer-copy fallita (P2P disabilitato, rifiuto del driver, + * NVLink giu'). Se la residenza era attiva il dato autorevole e' + * ancora sul device PRECEDENTE, non in dst: va scaricato da li'. + * Con x_dev_on<0 ha fallito l'upload e x host e' gia' autorevole. */ + if(x_dev_on>=0) coli_cuda_pipe_download(x_dev_on,x_dev,x,xb); + x_dev_on=-1; + } } } else if(x_dev_on>=0){ /* layer fuori pipe: il residuo torna a casa */ coli_cuda_pipe_download(x_dev_on,x_dev,x,xb); From 6dc1ee048cabe0eb9c34e1391ac2624be2a7ca15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E5=AE=A3=E5=B2=90?= Date: Fri, 24 Jul 2026 00:27:59 +0800 Subject: [PATCH 33/96] router: guard best==-1 in top-K selection (NaN logits caused OOB read/writes) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit All three top-K selection loops in moe() start from best=-1 and use it as an index as soon as the scan ends. choice[e] = sigmoidf(logit[e]) + bias and sigmoidf(NaN) is NaN, so `choice[e] > bv` is false for every e and best stays -1. The result was a heap OOB read of logits_all[s*E - 1], three OOB writes at index -1 (eusage / eheat / elast), a stack buffer underflow on the VLA seen[E] in FASE B, and uniq[]=-1 reaching expert_load(). This is the same threat model c/tests/test_logit_nan.c already documents (a bad streamed expert tile, or an fp overflow at a low-RAM eviction boundary). That regression hardened sampling; the router is upstream of it, so a NaN corrupts memory before argmax_v ever sees it. The EXPERT_BUDGET selection loop below already had the `if(best<0)` guard. router_best_or_fallback() degrades a failed scan to a deterministic in-range expert and warns once. Distinct kk map to distinct experts, so a slot is never duplicated. The pre-routed GPU shortcut is range-checked too: it feeds the same accounting arrays and seen[] without validation. Adds tests/test_router_nan.c — asserts the all-NaN scan really does leave best==-1, and that nothing out of range reaches the accounting arrays after the guard. No model file needed. Fixes #558 --- c/Makefile | 5 ++- c/colibri.c | 36 ++++++++++++++++++ c/tests/test_router_nan.c | 79 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 119 insertions(+), 1 deletion(-) create mode 100644 c/tests/test_router_nan.c diff --git a/c/Makefile b/c/Makefile index c7175681..c61d6485 100644 --- a/c/Makefile +++ b/c/Makefile @@ -199,7 +199,7 @@ else PYTHON ?= python3 endif CUDA_OBJ = -TEST_BINS = tests/test_json$(EXE) tests/test_st$(EXE) tests/test_st_mirror$(EXE) tests/test_st_pread$(EXE) tests/test_tier$(EXE) tests/test_grammar$(EXE) tests/test_schema_gbnf$(EXE) tests/test_decode_batch$(EXE) tests/test_idot$(EXE) tests/test_i4_grouped$(EXE) tests/test_stops$(EXE) tests/test_topp$(EXE) tests/test_sample_nan$(EXE) tests/test_temp_env$(EXE) tests/test_tok_o200k$(EXE) tests/test_kv_alloc$(EXE) tests/test_int3$(EXE) tests/test_int3_load$(EXE) tests/test_i4_acc512$(EXE) tests/test_compat_direct$(EXE) tests/test_dsa_select$(EXE) tests/test_logit_nan$(EXE) tests/test_pipe_block$(EXE) tests/test_e8_kernel$(EXE) +TEST_BINS = tests/test_json$(EXE) tests/test_st$(EXE) tests/test_st_mirror$(EXE) tests/test_st_pread$(EXE) tests/test_tier$(EXE) tests/test_grammar$(EXE) tests/test_schema_gbnf$(EXE) tests/test_decode_batch$(EXE) tests/test_idot$(EXE) tests/test_i4_grouped$(EXE) tests/test_stops$(EXE) tests/test_topp$(EXE) tests/test_sample_nan$(EXE) tests/test_temp_env$(EXE) tests/test_tok_o200k$(EXE) tests/test_kv_alloc$(EXE) tests/test_int3$(EXE) tests/test_int3_load$(EXE) tests/test_i4_acc512$(EXE) tests/test_compat_direct$(EXE) tests/test_dsa_select$(EXE) tests/test_logit_nan$(EXE) tests/test_router_nan$(EXE) tests/test_pipe_block$(EXE) tests/test_e8_kernel$(EXE) ifneq (,$(LINUX)) TEST_BINS += tests/test_uring$(EXE) endif @@ -435,6 +435,9 @@ tests/test_int3_load$(EXE): tests/test_int3_load.c colibri.c st.h uring.h json.h tests/test_logit_nan$(EXE): tests/test_logit_nan.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h $(CC) $(CFLAGS) $< -o $@ $(LDFLAGS) +tests/test_router_nan$(EXE): tests/test_router_nan.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h + $(CC) $(CFLAGS) $< -o $@ $(LDFLAGS) + tests/test_i4_acc512$(EXE): tests/test_i4_acc512.c $(CC) $(CFLAGS) $< -o $@ $(LDFLAGS) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..dfc05932 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -2715,6 +2715,22 @@ static int expert_is_resident(Model *m, int layer, int eid){ return 0; } +/* I loop di selezione top-K partono da best=-1 e lo usano come indice appena il + * giro finisce. Se OGNI punteggio candidato e' non finito nessun confronto riesce + * (NaN>bv e' falso) e best resta -1: logit[-1] e' una lettura fuori range, e + * idx[]=-1 si propaga a eusage/eheat/elast (tre scritture heap a indice -1) e al + * VLA seen[] (underflow di stack). Non e' teorico: c/tests/test_logit_nan.c + * documenta NaN/Inf nei logit da un tile expert corrotto o da un overflow fp al + * confine di eviction, e ha indurito solo il lato sampling — il router e' a monte. + * Qui degradiamo a una scelta deterministica e avvisiamo una volta sola. */ +static int router_best_or_fallback(int best, int kk, int E, int layer){ + if(best>=0) return best; + static int warned; + if(!warned){ warned=1; + fprintf(stderr,"[router] logits non finiti al layer %d: selezione degradata\n",layer); } + return kkK) keff[s]=K; + for(int kk=0;kk=E){ + static int warned_pre; + if(!warned_pre){ warned_pre=1; + fprintf(stderr,"[router] indice expert %d fuori range al layer %d: azzerato\n",e,layer); } + idxs[(int64_t)s*K+kk]=0; + } + } + } for(int s=0;sereq+=keff[s]; for(int kk=0;kkbv){bv=choice[e];best=e;} } + best=router_best_or_fallback(best,kk,E,layer); rank_buf[kk]=best; rank_w[kk]=logit[best]; } float tot=1e-20f; for(int kk=0;kk0?rank_w[kk]:0; @@ -2813,6 +2847,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int for(int kk=0;kkbv){bv=choice[e];best=e;} } + best=router_best_or_fallback(best,kk,E,layer); rank_buf[kk]=best; rank_w[kk]=logit[best]; } } @@ -2881,6 +2916,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int for(int kk=0;kkbv){bv=choice[e];best=e;} } + best=router_best_or_fallback(best,kk,E,layer); idx[kk]=best; w[kk]=logit[best]; } if(g_route_agree){ diff --git a/c/tests/test_router_nan.c b/c/tests/test_router_nan.c new file mode 100644 index 00000000..0f439e78 --- /dev/null +++ b/c/tests/test_router_nan.c @@ -0,0 +1,79 @@ +/* Regression for non-finite-logit poisoning of the ROUTER (upstream of sampling). + * + * test_logit_nan.c hardened the sampling side. The routing side was not, and it + * runs first: the three top-K selection loops in moe() start from best=-1 and use + * it as an index as soon as the scan ends. choice[e] = sigmoidf(logit[e]) + bias, + * and sigmoidf(NaN) is NaN, so `choice[e] > bv` is false for every e — best stays + * -1. The old code then did idx[kk]=best; w[kk]=logit[best], which is: + * + * - a heap OOB read of logits_all[s*E - 1] at s==0; + * - three OOB writes at index -1 (eusage / eheat / elast); + * - a stack buffer underflow on the VLA `unsigned char seen[E]` in FASE B; + * - uniq[nu++] = -1 handed to expert_load(). + * + * Fix under test: router_best_or_fallback() maps a failed scan to a deterministic + * in-range expert and warns once. Degrade + diagnose, never index out of range. + * + * No model file needed: exercises the guard and the exact scan that trips it. */ +#include +#include +#define main coli_glm_main_unused +#include "../colibri.c" +#undef main + +/* the selection scan as written in moe(), verbatim in shape */ +static int scan_best(const float *choice, int E, const int *taken, int ntaken){ + int best=-1; float bv=-1e30f; + for(int e=0;ebv){bv=choice[e];best=e;} + } + return best; +} + +int main(void){ + const int E=8; + + /* --- the trigger is real: an all-NaN choice[] leaves best==-1 --- */ + { float choice[8]; for(int e=0;e=0 && b distinct expert, so a slot is never duplicated"); + } + /* Ksel may exceed E on a malformed config: still in range, never -1 */ + for(int kk=E;kk=0 && b=0 && idx[kk] Date: Fri, 24 Jul 2026 00:29:26 +0800 Subject: [PATCH 34/96] moe: route the eight hot-path allocations through a checked alloc MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit moe() allocated idxs, ws, keff, uniq, is_hit, keep, rows, rw, group_row, group_weight, mrows and mrw with bare malloc/calloc and dereferenced each immediately. This is the hottest function in the engine and running at the memory ceiling is the project's premise — the exact condition under which malloc returns NULL. It was also inconsistent inside the same function: rank_buf / rank_w fifty lines above are checked and degrade gracefully, the xexp scratch is checked and exits with a message, falloc() has an anti-wrap guard and an OOM exit, and expert_load_impl() goes out of its way to make OOM survivable for speculative loads. Only the hottest allocations could segfault mid-generation with no diagnostic — indistinguishable from a real crash bug in a user report. xalloc/xzalloc mirror the existing falloc convention: message + exit(1). Graceful degradation is not attempted; a named diagnostic instead of SIGSEGV is the whole point. Fixes #559 --- c/colibri.c | 37 ++++++++++++++++++++++++++++--------- 1 file changed, 28 insertions(+), 9 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..fe140a85 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -461,6 +461,25 @@ static float *falloc(int64_t n){ if(n<0 || (uint64_t)n > SIZE_MAX/sizeof(float)){ fprintf(stderr,"falloc: n=%lld is out of range\n",(long long)n); exit(1); } float *p=malloc((size_t)n*sizeof(float)); if(!p){fprintf(stderr,"OOM\n");exit(1);} return p; } +/* Come falloc, per i buffer non-float del percorso caldo. moe() e' la funzione piu' + * chiamata del motore e girare al soffitto di RAM e' la premessa del progetto: e' + * esattamente la condizione in cui malloc torna NULL. Un deref di NULL li' e' un + * segfault a meta' generazione senza diagnostica, indistinguibile da un bug vero + * quando l'utente lo riporta. exit(1) con messaggio e' la stessa convenzione di + * falloc e dello scratch xexp. */ +static void *xalloc(size_t n, const char *what){ + if(n==0) n=1; + void *p=malloc(n); + if(!p){ fprintf(stderr,"OOM: %s (%zu byte)\n",what,n); exit(1); } + return p; +} +static void *xzalloc(size_t n, const char *what){ + if(n==0) n=1; + void *p=calloc(n,1); + if(!p){ fprintf(stderr,"OOM: %s (%zu byte)\n",what,n); exit(1); } + return p; +} + /* Fused gate+up for grouped int4 (fmt=4): computes both yg[S,O] and yu[S,O] from @@ -2750,8 +2769,8 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int } /* ---- FASE A: routing di tutte le S posizioni ---- */ double route_t0=g_prof?now_s():0; - int *idxs=malloc((size_t)S*K*sizeof(int)); float *ws=malloc((size_t)S*K*sizeof(float)); - int *keff=malloc(S*sizeof(int)); + int *idxs=xalloc((size_t)S*K*sizeof(int),"moe idxs"); float *ws=xalloc((size_t)S*K*sizeof(float),"moe ws"); + int *keff=xalloc((size_t)S*sizeof(int),"moe keff"); /* router in UN matmul batch: stessa matematica, via le S chiamate S=1 */ float *logits_all=falloc((int64_t)S*E); int pre_routed=0; (void)pre_routed; @@ -2937,7 +2956,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int } m->enr[layer]=keff[S-1]; for(int kk=0;kkeroute[layer][kk]=idxs[(int64_t)(S-1)*K+kk]; /* ---- FASE B: union degli expert del batch ---- */ - int *uniq=malloc((size_t)E*sizeof(int)); int nu=0; + int *uniq=xalloc((size_t)E*sizeof(int),"moe uniq"); int nu=0; unsigned char seen[E]; memset(seen,0,(size_t)E); for(int s=0;spin[layer]; @@ -2975,7 +2994,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int int miss_budget = g_expert_budget - nhits; if(miss_budget<0) miss_budget=0; /* mark which unique experts to keep (1) or drop (0): keep all hits, fill rest * with top-weight misses up to miss_budget */ - unsigned char *keep=calloc(nu,1); int nkeep=0; + unsigned char *keep=xzalloc((size_t)nu,"moe keep"); int nkeep=0; for(int j=0;j0){ double t0=now_s(); From 1cc12c06bfbaea0776f39ac136c724d7a2635117 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E5=AE=A3=E5=B2=90?= Date: Fri, 24 Jul 2026 00:31:17 +0800 Subject: [PATCH 35/96] EXPERT_BUDGET: never trim a position to zero experts; compensate weights without norm_topk MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two gaps in the same block. Both changed the mathematics of the MoE output rather than only reducing disk I/O, which is what the knob is documented to do. 1. A position could end with keff[s] == 0. keep[] is seeded with every cache hit before any miss is considered, so once nhits >= g_expert_budget the miss budget is 0 and no miss survives. A position whose entire top-K is misses compacts to w == 0 — the existing `w>0` guard already conceded this happens. That token got only the shared expert, and the wrong hidden state went into the KV cache and poisoned every subsequent token: the same failure mode the block's comment describes for prefill (#292), reached from another direction. S=2, K=1, EXPERT_BUDGET=1 with two different missed experts is enough. Each position now keeps its highest-gate-weight expert, counted as "rescued" in STATS so a too-tight budget is visible instead of silent. 2. Weights were only renormalised when norm_topk is set. With norm_topk == 0 the weights are not normalised in the first place, so dropping experts shrank the routed contribution by whatever gate mass was discarded. GLM-5.2 sets norm_topk=1 so the flagship path was unaffected; every other MoE config had EXPERT_BUDGET silently attenuating the layer. Now rescaled by old_sum/new_sum, which is invariant to routed_scale and holds the magnitude constant. The norm_topk arm is unchanged and correct — added a comment saying why the divide-then-multiply is not double-scaling, since it reads like a bug at first glance. Fixes #560 --- c/colibri.c | 46 ++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 42 insertions(+), 4 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..1c3f8e26 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -592,6 +592,7 @@ static int g_expert_budget=0; /* EXPERT_BUDGET=N -> cap distinct experts loaded * lowest-gate-weight experts from the cross-position union. MoE-Spec * (arXiv 2602.16052): top-32 of 64 capture 93% routing weight. */ static int64_t g_budget_dropped=0; /* total experts dropped by EXPERT_BUDGET across all layers */ +static int64_t g_budget_rescued=0; /* experts re-kept because a position would have been left with zero */ /* CACHE_ROUTE (paper 2412.00099 max-rank): opt-in only. Keep true top-J always; * fill remaining slots preferring pin∪LRU experts ranked within top-M (or mass ROUTE_P). */ static int g_cache_route=0; @@ -2985,21 +2986,55 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int /* build a lookup: for each expert id, is it kept? (reuse seen[]) */ memset(seen,0,(size_t)E); for(int j=0;j=g_expert_budget il miss_budget e' 0 e una + * posizione il cui top-K e' fatto solo di miss viene compattata a w==0 (il + * guard `w>0` qui sotto lo ammette). Quel token riceverebbe solo lo shared + * expert, e lo stato nascosto sbagliato finisce nella KV cache avvelenando + * ogni token successivo — la stessa modalita' di guasto descritta sopra per + * il prefill (#292), raggiunta da un'altra direzione. Bastano S=2, K=1, + * EXPERT_BUDGET=1 e due miss diversi. Ripeschiamo il miss col peso di gate + * piu' alto della posizione e lo contiamo in STATS. */ + for(int s=0;sbw){ bw=wv; be=idxs[(int64_t)s*K+kk]; } + } + if(be<0) be=idxs[(int64_t)s*K]; /* pesi tutti non finiti: primo della lista */ + seen[be]=1; g_budget_rescued++; + for(int j=0;jnorm_topk && w>0){ + /* sm porta gia' routed_scale (applicato in FASE A): la divisione lo + * cancella e il multiply qui sotto lo riapplica — non e' doppio scaling */ float sm=0; for(int kk=0;kkrouted_scale; + } else if(w>0 && snew>1e-20f && sold>snew){ + /* Senza norm_topk i pesi NON sono normalizzati: scartare expert + * ridurrebbe la magnitudine del contributo routed di tutta la massa + * di gate buttata via, cioe' il budget cambierebbe la matematica del + * layer invece di risparmiare solo I/O. Riscaliamo per old/new (il + * rapporto e' invariante a routed_scale) cosi' la magnitudine resta. + * GLM-5.2 usa norm_topk=1, quindi questo tocca solo le altre config MoE. */ + float sc=sold/snew; + for(int kk=0;kkereq/produced:0.0, (produced&&nsp)?(double)m->ereq/produced/nsp:0.0, nsp, c->topk, g_topk, g_topp); if(g_cache_route) printf(" | CACHE_ROUTE J=%d M=%d P=%.2f alpha=%.2f", g_route_j, g_route_m, g_route_p, g_route_alpha); - if(g_expert_budget) printf(" | EXPERT_BUDGET=%d (dropped %lld experts, ~%.1f GB I/O saved)", g_expert_budget, (long long)g_budget_dropped, g_budget_dropped*18.9e6/1e9); + if(g_expert_budget){ + printf(" | EXPERT_BUDGET=%d (dropped %lld experts, ~%.1f GB I/O saved)", g_expert_budget, (long long)g_budget_dropped, g_budget_dropped*18.9e6/1e9); + if(g_budget_rescued) printf(" [%lld rescued: budget too tight, position would have had 0 routed experts]", (long long)g_budget_rescued); + } printf("\n"); printf("speculation: %.2f tokens/forward (%llu forwards per %llu tokens) | MTP acceptance %.0f%% (%llu/%llu)\n", m->n_fw?(double)m->n_emit/m->n_fw:1.0, (unsigned long long)m->n_fw, (unsigned long long)m->n_emit, From aabb4eb256a5decae112a1435e3fe3a88d3248e3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E5=AE=A3=E5=B2=90?= Date: Fri, 24 Jul 2026 00:32:36 +0800 Subject: [PATCH 36/96] COLI_GROUP_ASYNC: don't recompute a device whose async group already succeeded MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The async path issued every device's expert group, took the results and copied them into group_y — and then the synchronous fallback loop ran anyway on the same devices and recomputed everything. `if(!async_done)` was written without braces, so it guarded only the single `if(g_pres_home>=0 && S==1){...}` statement that follows it. The OpenMP loop after that was never in its scope, and its own guard was `dev_ok[di]!=2`. Value 2 means "handled on device, skip host collection" and is only set by the resident path; a device that SUCCEEDED on the async path carries dev_ok==1, which passed the guard. Output stayed correct — it is the same computation — which is presumably why this went unnoticed. The symptom was that COLI_GROUP_ASYNC never showed a speedup while roughly doubling GPU expert work. Guard is now dev_ok[di]==0, the actual "needs the synchronous host round-trip" condition: it covers both the never-attempted case and an async per-device take() failure, and excludes 1 (async succeeded) and 2 (resident path owns it). dev_ok is zero-initialised, so the plain non-async path is unaffected. Braces added so the scope is unambiguous. Not measured on hardware — no CUDA host available here; the fix is from the control-flow reading above. Worth an Nsight check that each layer now shows one expert_group batch per device per call instead of two. Fixes #561 --- c/colibri.c | 37 ++++++++++++++++++++++--------------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb24c831..520697e1 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -3430,25 +3430,32 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int } else for(int di=0;di=0 && S==1){ - for(int di=0;di=0 && S==1){ + for(int di=0;di1) schedule(static) - for(int di=0;di Date: Thu, 23 Jul 2026 22:37:45 +0530 Subject: [PATCH 37/96] serve: split Anthropic thinking response blocks --- c/openai_server.py | 159 +++++++++++++++++++++++++---- c/tests/test_anthropic_messages.py | 87 +++++++++++++++- 2 files changed, 224 insertions(+), 22 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 6b8632de..e09e1f8f 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -421,7 +421,13 @@ def render_chat(messages, enable_thinking=False, reasoning_effort=None, tools=No # content may be null when the message is purely tool_calls raw = message.get("content") text = content_text(raw, f"messages.{index}.content") if raw is not None else "" - prompt.append(f"<|assistant|>{text.strip()}") + reasoning = message.get("reasoning_content") + if reasoning is None: + reasoning = "" + elif not isinstance(reasoning, str): + raise APIError(400, "`reasoning_content` must be a string.", + f"messages.{index}.reasoning_content") + prompt.append(f"<|assistant|>{reasoning}{text.strip()}") for tc in (message.get("tool_calls") or []): fn = tc.get("function", tc) if isinstance(tc, dict) else {} args = fn.get("arguments", "{}") @@ -455,6 +461,63 @@ def render_chat(messages, enable_thinking=False, reasoning_effort=None, tools=No # rendering, scheduling, generation and tool parsing stay single-sourced. Only the request # translation and the response/SSE shapes are new. Claude Code is the reference client. +ANTHROPIC_LOCAL_SIGNATURE = "colibri-local" # opaque compatibility metadata, not a crypto proof + + +class ThinkingStreamSplit: + """Split GLM's reasoning marker without leaking markers across stream chunks.""" + MARKERS = (THINK_OPEN, THINK_CLOSE) + + def __init__(self, on_thinking, on_text, on_thinking_end=None): + self.on_thinking = on_thinking + self.on_text = on_text + self.on_thinking_end = on_thinking_end + self.thinking = True + self.buf = "" + + def _emit(self, text): + if text: + (self.on_thinking if self.thinking else self.on_text)(text) + + def feed(self, chunk): + self.buf += chunk + while True: + hits = [(offset, marker) for marker in self.MARKERS + if (offset := self.buf.find(marker)) >= 0] + if hits: + offset, marker = min(hits, key=lambda hit: hit[0]) + self._emit(self.buf[:offset]) + self.buf = self.buf[offset + len(marker):] + if marker == THINK_CLOSE and self.thinking: + self.thinking = False + if self.on_thinking_end: + self.on_thinking_end() + continue + + hold = 0 + for size in range(1, min(len(self.buf), max(map(len, self.MARKERS)) - 1) + 1): + if any(marker.startswith(self.buf[-size:]) for marker in self.MARKERS): + hold = size + flush = len(self.buf) - hold + if flush: + self._emit(self.buf[:flush]) + self.buf = self.buf[flush:] + return + + def finish(self): + self._emit(self.buf) + self.buf = "" + + +def split_thinking_reply(text): + """Return the marker-free (thinking, answer) portions of one GLM reply.""" + thinking, answer = [], [] + split = ThinkingStreamSplit(thinking.append, answer.append) + split.feed(text) + split.finish() + return "".join(thinking), "".join(answer) + + def _anthropic_block_text(blocks, param): """Text out of an Anthropic content array (tool_result content is the same shape).""" if isinstance(blocks, str): @@ -504,7 +567,7 @@ def anthropic_to_openai(body): if not isinstance(content, list): raise APIError(400, "Message content must be a string or an array of blocks.", f"messages.{index}.content") - texts, calls, results = [], [], [] + texts, reasoning, calls, results = [], [], [], [] for j, block in enumerate(content): where = f"messages.{index}.content.{j}" if not isinstance(block, dict): @@ -514,6 +577,17 @@ def anthropic_to_openai(body): if not isinstance(block.get("text"), str): raise APIError(400, "Text blocks require a string `text` field.", f"{where}.text") texts.append(block["text"]) + elif kind == "thinking": + if role != "assistant": + raise APIError(400, "`thinking` blocks are valid only in assistant messages.", + f"{where}.type", "unsupported_content_type") + if not isinstance(block.get("thinking"), str): + raise APIError(400, "Thinking blocks require a string `thinking` field.", + f"{where}.thinking") + if not isinstance(block.get("signature"), str): + raise APIError(400, "Thinking blocks require a string `signature` field.", + f"{where}.signature") + reasoning.append(block["thinking"]) elif kind == "tool_use": name = block.get("name") if not isinstance(name, str) or not name: @@ -539,8 +613,10 @@ def anthropic_to_openai(body): messages.extend(results) text = "".join(texts) if role == "assistant": - if text or calls: + if text or reasoning or calls: entry = {"role": "assistant", "content": text or None} + if reasoning: + entry["reasoning_content"] = "".join(reasoning) if calls: entry["tool_calls"] = calls messages.append(entry) @@ -1462,9 +1538,9 @@ def anthropic_messages(self, body, request_id): tools = None prompt = render_chat(messages, enable_thinking, "high" if enable_thinking else None, tools, tool_choice) - self.anthropic_generation(translated, prompt, request_id, tools) + self.anthropic_generation(translated, prompt, request_id, tools, enable_thinking) - def anthropic_generation(self, body, prompt, request_id, tools): + def anthropic_generation(self, body, prompt, request_id, tools, enable_thinking): maximum, temperature, top_p, grammar = generation_options(body, self.server.max_tokens) cache_slot = body.get("cache_slot") if (cache_slot is not None and @@ -1479,10 +1555,14 @@ def anthropic_generation(self, body, prompt, request_id, tools): def blocks_and_stop(text, stats): """Split a finished reply into Anthropic content blocks + stop_reason.""" + content = [] + if enable_thinking: + reasoning, text = split_thinking_reply(text) + content.append({"type": "thinking", "thinking": reasoning, + "signature": ANTHROPIC_LOCAL_SIGNATURE}) calls = [] if tools: text, calls = parse_tool_calls(text, tools) - content = [] if text: content.append({"type": "text", "text": text}) for call in calls: @@ -1553,8 +1633,15 @@ def keepalive(): "id": message_id, "type": "message", "role": "assistant", "model": self.server.model_id, "content": [], "stop_reason": None, "stop_sequence": None, "usage": {"input_tokens": 0, "output_tokens": 0}}}) - send_event("content_block_start", {"type": "content_block_start", "index": 0, - "content_block": {"type": "text", "text": ""}}) + text_index = 1 if enable_thinking else 0 + stream_state = {"thinking_closed": not enable_thinking, + "text_started": not enable_thinking} + if enable_thinking: + send_event("content_block_start", {"type": "content_block_start", "index": 0, + "content_block": {"type": "thinking", "thinking": "", "signature": ""}}) + else: + send_event("content_block_start", {"type": "content_block_start", "index": 0, + "content_block": {"type": "text", "text": ""}}) ka_thread = threading.Thread(target=keepalive, daemon=True) ka_thread.start() @@ -1562,11 +1649,19 @@ def keepalive(): state = {"buf": "", "in_tool": False} hold = len(BOX_START) - 1 - def on_text(chunk): - raw.append(chunk) + def emit_text(chunk): + if not chunk: + return + if not stream_state["text_started"]: + stream_state["text_started"] = True + send_event("content_block_start", {"type": "content_block_start", + "index": text_index, "content_block": {"type": "text", "text": ""}}) + send_event("content_block_delta", {"type": "content_block_delta", + "index": text_index, "delta": {"type": "text_delta", "text": chunk}}) + + def emit_answer(chunk): if not tools: - send_event("content_block_delta", {"type": "content_block_delta", "index": 0, - "delta": {"type": "text_delta", "text": chunk}}) + emit_text(chunk) return if state["in_tool"]: return # tool markers never reach the client as text @@ -1574,32 +1669,54 @@ def on_text(chunk): cut = state["buf"].find(BOX_START) if cut >= 0: if cut: - send_event("content_block_delta", {"type": "content_block_delta", "index": 0, - "delta": {"type": "text_delta", "text": state["buf"][:cut]}}) + emit_text(state["buf"][:cut]) state["buf"] = "" state["in_tool"] = True return flush = max(0, len(state["buf"]) - hold) if flush: - send_event("content_block_delta", {"type": "content_block_delta", "index": 0, - "delta": {"type": "text_delta", "text": state["buf"][:flush]}}) + emit_text(state["buf"][:flush]) state["buf"] = state["buf"][flush:] + def emit_thinking(chunk): + send_event("content_block_delta", {"type": "content_block_delta", "index": 0, + "delta": {"type": "thinking_delta", "thinking": chunk}}) + + def close_thinking(): + if stream_state["thinking_closed"]: + return + stream_state["thinking_closed"] = True + send_event("content_block_delta", {"type": "content_block_delta", "index": 0, + "delta": {"type": "signature_delta", + "signature": ANTHROPIC_LOCAL_SIGNATURE}}) + send_event("content_block_stop", {"type": "content_block_stop", "index": 0}) + + split = (ThinkingStreamSplit(emit_thinking, emit_answer, close_thinking) + if enable_thinking else None) + + def on_text(chunk): + raw.append(chunk) + (split.feed if split else emit_answer)(chunk) + stats = self.server.engine.generate( prompt, maximum, temperature, top_p, on_text, cache_slot, lambda: not connected[0], grammar=grammar) + if split: + split.finish() + close_thinking() # budget exhaustion before if tools and not state["in_tool"] and state["buf"]: - send_event("content_block_delta", {"type": "content_block_delta", "index": 0, - "delta": {"type": "text_delta", "text": state["buf"]}}) + emit_text(state["buf"]) ka_stop.set() ka_thread.join(timeout=2) - send_event("content_block_stop", {"type": "content_block_stop", "index": 0}) + if stream_state["text_started"]: + send_event("content_block_stop", {"type": "content_block_stop", + "index": text_index}) content, stop_reason = blocks_and_stop("".join(raw), stats) - index = 1 + index = text_index + 1 if stream_state["text_started"] else 1 for block in content: if block["type"] != "tool_use": - continue # text already streamed as block 0 + continue # thinking/text blocks were streamed above send_event("content_block_start", {"type": "content_block_start", "index": index, "content_block": {"type": "tool_use", "id": block["id"], "name": block["name"], "input": {}}}) diff --git a/c/tests/test_anthropic_messages.py b/c/tests/test_anthropic_messages.py index 08dff69f..b34c00a3 100644 --- a/c/tests/test_anthropic_messages.py +++ b/c/tests/test_anthropic_messages.py @@ -16,7 +16,8 @@ from urllib.error import HTTPError from urllib.request import Request, urlopen -from openai_server import (APIServer, anthropic_to_openai, anthropic_tools, APIError) +from openai_server import (APIServer, anthropic_to_openai, anthropic_tools, APIError, + render_chat) class FakeEngine: @@ -73,6 +74,19 @@ def test_tool_result_only_message_adds_no_empty_user_turn(self): ]}) self.assertEqual([m["role"] for m in messages], ["user", "assistant", "tool"]) + def test_prior_assistant_thinking_block_is_preserved(self): + messages = anthropic_to_openai({"messages": [ + {"role": "user", "content": "question"}, + {"role": "assistant", "content": [ + {"type": "thinking", "thinking": "reasoning", + "signature": "opaque-upstream-signature"}, + {"type": "text", "text": "answer"}]}, + {"role": "user", "content": "follow-up"}, + ]}) + self.assertEqual(messages[1]["reasoning_content"], "reasoning") + self.assertIn("<|assistant|>reasoninganswer", + render_chat(messages)) + def test_tools_and_choice_translate(self): tools, choice = anthropic_tools({ "tools": [{"name": "f", "description": "d", @@ -208,6 +222,77 @@ def test_thinking_enabled_renders_reasoning_prompt(self): self.assertIn("Reasoning Effort", self.engine.prompts[-1]) self.assertTrue(self.engine.prompts[-1].endswith("<|assistant|>")) + def test_thinking_enabled_returns_separate_blocks(self): + self.engine.script = ("reasoninganswer",) + with self.post(self.base_body(thinking={"type": "enabled"})) as response: + payload = json.load(response) + self.assertEqual(payload["content"], [ + {"type": "thinking", "thinking": "reasoning", "signature": "colibri-local"}, + {"type": "text", "text": "answer"}, + ]) + + def test_streamed_thinking_marker_can_split_at_every_boundary(self): + marker = "" + for boundary in range(1, len(marker)): + with self.subTest(boundary=boundary): + self.engine.script = ("reasoning" + marker[:boundary], + marker[boundary:] + "answer") + with self.post(self.base_body(stream=True, + thinking={"type": "enabled"})) as response: + raw = response.read().decode() + payloads = [json.loads(line[len("data: "):]) for line in raw.splitlines() + if line.startswith("data: ")] + starts = [p for p in payloads if p["type"] == "content_block_start"] + self.assertEqual([(p["index"], p["content_block"]["type"]) for p in starts], + [(0, "thinking"), (1, "text")]) + deltas = [p for p in payloads if p["type"] == "content_block_delta"] + self.assertEqual("".join(p["delta"].get("thinking", "") for p in deltas), + "reasoning") + self.assertEqual("".join(p["delta"].get("text", "") for p in deltas), + "answer") + self.assertEqual([p["delta"]["type"] for p in deltas + if p["delta"]["type"] == "signature_delta"], + ["signature_delta"]) + signature = next(i for i, p in enumerate(payloads) + if p.get("delta", {}).get("type") == "signature_delta") + self.assertEqual(payloads[signature]["delta"]["signature"], "colibri-local") + thinking_stop = next(i for i, p in enumerate(payloads) + if p["type"] == "content_block_stop" and p["index"] == 0) + text_start = next(i for i, p in enumerate(payloads) + if p["type"] == "content_block_start" and p["index"] == 1) + self.assertLess(signature, thinking_stop) + self.assertLess(thinking_stop, text_start) + self.assertNotIn(marker, raw) + + def test_thinking_budget_exhaustion_returns_thinking_only(self): + self.engine.script = ("unfinished reasoning",) + self.engine.length_limited = True + with self.post(self.base_body(thinking={"type": "enabled"})) as response: + payload = json.load(response) + self.assertEqual(payload["content"], [ + {"type": "thinking", "thinking": "unfinished reasoning", + "signature": "colibri-local"}, + ]) + self.assertEqual(payload["stop_reason"], "max_tokens") + + def test_streamed_thinking_then_tool_uses_next_block(self): + self.engine.script = ("reasoningfx" + "1") + body = self.base_body(stream=True, thinking={"type": "enabled"}, + tools=[{"name": "f", "input_schema": { + "type": "object", + "properties": {"x": {"type": "string"}}}}]) + with self.post(body) as response: + raw = response.read().decode() + payloads = [json.loads(line[len("data: "):]) for line in raw.splitlines() + if line.startswith("data: ")] + starts = [p for p in payloads if p["type"] == "content_block_start"] + self.assertEqual([(p["index"], p["content_block"]["type"]) for p in starts], + [(0, "thinking"), (1, "tool_use")]) + self.assertNotIn("", raw) + self.assertEqual(payloads[-2]["delta"]["stop_reason"], "tool_use") + def test_unsupported_fields_refuse_loudly(self): for field, value in (("stop_sequences", ["STOP"]), ("top_k", 40)): with self.assertRaises(HTTPError) as caught: From 857f725a50291d36851891eee07905a963e905bb Mon Sep 17 00:00:00 2001 From: Marco Godoy Date: Thu, 23 Jul 2026 12:48:54 -0400 Subject: [PATCH 38/96] build: portable build_cuda.bat with MSVC/CUDA/arch auto-detection Replace the hardcoded-path build script (a specific user's Desktop path, CUDA v12.8, fixed VS location) with one that discovers what it needs, so coli_cuda.dll can be built by double-click or from any shell. - MSVC: find the host compiler with vswhere, preferring the 2022 toolset (range [17.0,18.0)) and falling back to -latest. vswhere is invoked by bare name from its own directory so its quoted path plus the quoted version arg don't confuse for /f's implicit cmd /c. - CUDA: use nvcc from PATH, else %CUDA_PATH%; add -L\lib\x64 only when the toolkit root is known. - Arch: auto-detect compute capability from GPU 0 via nvidia-smi (12.0 -> sm_120, 8.9 -> sm_89), validated before use; default sm_120. CUDA_ARCH overrides for cross-compiling. - Build in %~dp0 so backend_cuda.cu resolves and the DLL lands next to colibri.exe. All paths quoted, so spaces in any path are handled. CCBIN and ALLOW_UNSUPPORTED knobs for edge cases. --- c/build_cuda.bat | 136 +++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 132 insertions(+), 4 deletions(-) diff --git a/c/build_cuda.bat b/c/build_cuda.bat index dc19f09d..0b332ecc 100644 --- a/c/build_cuda.bat +++ b/c/build_cuda.bat @@ -1,5 +1,133 @@ @echo off -call "C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Auxiliary\Build\vcvars64.bat" -cd /d C:\Users\Mark\Desktop\Projects\colibri\c -"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.8\bin\nvcc" -O3 -std=c++17 -arch=sm_120 -Xcompiler=-W3 -shared -DCOLI_CUDA_BUILDING_DLL -L"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.8\lib/x64" -lcudart backend_cuda.cu -o coli_cuda.dll -echo EXITCODE=%ERRORLEVEL% +rem --------------------------------------------------------------------------- +rem build_cuda.bat - one-shot build of coli_cuda.dll (Windows, nvcc + MSVC). +rem +rem Mirrors the Makefile's `cuda-dll` recipe so it can be double-clicked or run +rem from a plain shell: it locates vcvars64.bat and the CUDA toolkit itself +rem rather than hardcoding install paths. +rem +rem Overrides (set before calling): +rem CUDA_ARCH GPU compute capability (sm_NNN). Auto-detected from the +rem installed GPU via nvidia-smi; set to override or to +rem cross-compile. sm_120 = Blackwell / RTX 50xx, sm_89 = Ada / +rem RTX 40xx, sm_86 = Ampere / RTX 30xx. +rem CUDA_PATH CUDA toolkit root. Normally set by the CUDA installer. +rem CCBIN Directory containing the cl.exe nvcc should use as host +rem compiler. Set this to force a specific toolset. +rem ALLOW_UNSUPPORTED =1 adds -allow-unsupported-compiler. +rem --------------------------------------------------------------------------- +setlocal + +rem --- GPU arch: detect from the installed GPU unless overridden ------------ +rem nvidia-smi reports compute capability as e.g. "12.0"; strip the dot for the +rem sm_NNN suffix (12.0 -> sm_120, 8.9 -> sm_89). The value is validated as +rem .; anything else falls back to sm_120. +if not "%CUDA_ARCH%"=="" goto have_arch +for /f "usebackq skip=1 delims=" %%c in (`nvidia-smi -i 0 --query-gpu=compute_cap --format=csv 2^>nul`) do set "CC=%%c" +echo(%CC%| findstr /r "^[0-9][0-9]*\.[0-9][0-9]*$" >nul 2>&1 +if errorlevel 1 goto arch_default +for /f "tokens=1,2 delims=." %%a in ("%CC%") do set "CUDA_ARCH=sm_%%a%%b" +if not "%CUDA_ARCH%"=="" goto have_arch +:arch_default +echo WARNING: could not detect GPU compute capability; defaulting to sm_120.>&2 +set "CUDA_ARCH=sm_120" +:have_arch +set "CC=" +set "VSINSTALLER=%ProgramFiles(x86)%\Microsoft Visual Studio\Installer" +set "VSWHERE=%VSINSTALLER%\vswhere.exe" + +rem --- MSVC: nvcc needs cl.exe as its host compiler ------------------------- +rem Prefer the VS 2022 toolset (range [17.0,18.0)); fall back to -latest if no +rem 2022 install exists. +if not "%CCBIN%"=="" goto have_msvc +where cl.exe >nul 2>&1 +if %ERRORLEVEL%==0 goto have_msvc + +if not exist "%VSWHERE%" goto no_msvc + +rem Invoke vswhere by bare name from its own directory. +set "VSDIR=" +pushd "%VSINSTALLER%" +for /f "usebackq delims=" %%i in (`vswhere.exe -products * -version "[17.0,18.0)" -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath`) do set "VSDIR=%%i" +if not "%VSDIR%"=="" goto found_vs +echo WARNING: no VS 2017-2022 found; falling back to the newest install.>&2 +echo If nvcc reports "unsupported Microsoft Visual Studio version",>&2 +echo install the 2022 toolset - see the error text at the end.>&2 +for /f "usebackq delims=" %%i in (`vswhere.exe -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath`) do set "VSDIR=%%i" +:found_vs +popd +if "%VSDIR%"=="" goto no_msvc +if not exist "%VSDIR%\VC\Auxiliary\Build\vcvars64.bat" goto no_msvc +call "%VSDIR%\VC\Auxiliary\Build\vcvars64.bat" >nul +where cl.exe >nul 2>&1 +if not %ERRORLEVEL%==0 goto no_msvc +:have_msvc + +rem -ccbin pins the host compiler explicitly. +set "CCBIN_FLAG=" +if not "%CCBIN%"=="" set "CCBIN_FLAG=-ccbin "%CCBIN%"" +set "UNSUPPORTED_FLAG=" +if "%ALLOW_UNSUPPORTED%"=="1" set "UNSUPPORTED_FLAG=-allow-unsupported-compiler" + +rem --- CUDA ----------------------------------------------------------------- +set "NVCC=nvcc" +where nvcc.exe >nul 2>&1 +if %ERRORLEVEL%==0 goto have_nvcc +if "%CUDA_PATH%"=="" goto no_cuda +if not exist "%CUDA_PATH%\bin\nvcc.exe" goto no_cuda +set "NVCC=%CUDA_PATH%\bin\nvcc.exe" +:have_nvcc + +rem Add -L\lib\x64 for cudart only when CUDA_PATH is known. +set "CUDA_LIB=" +if not "%CUDA_PATH%"=="" set "CUDA_LIB=-L"%CUDA_PATH%\lib\x64"" + +rem --- Build ---------------------------------------------------------------- +rem Build in the script's own directory so backend_cuda.cu resolves and the DLL +rem lands next to colibri.exe. +pushd "%~dp0" + +echo Building coli_cuda.dll for %CUDA_ARCH% ... +rem -Xcompiler=-W3 sets the MSVC host-compiler warning level. +"%NVCC%" -O3 -std=c++17 -arch=%CUDA_ARCH% -Xcompiler=-W3 -shared ^ + %CCBIN_FLAG% %UNSUPPORTED_FLAG% ^ + -DCOLI_CUDA_BUILDING_DLL %CUDA_LIB% -lcudart ^ + backend_cuda.cu -o coli_cuda.dll +set "RC=%ERRORLEVEL%" + +popd +if not "%RC%"=="0" goto failed +echo. +echo OK: %~dp0coli_cuda.dll +echo Now build the host with the runtime loader: +echo make colibri.exe CUDA_DLL=1 ARCH=native +exit /b 0 + +:no_msvc +echo ERROR: cl.exe (MSVC) not found.>&2 +echo Install "Desktop development with C++":>&2 +echo winget install Microsoft.VisualStudio.2022.BuildTools>&2 +echo ...or run this from the "x64 Native Tools Command Prompt for VS 2022".>&2 +exit /b 1 + +:no_cuda +echo ERROR: nvcc not found, and CUDA_PATH is unset or has no bin\nvcc.exe.>&2 +echo CUDA 12.8+ is required for sm_120 (Blackwell / RTX 50xx):>&2 +echo winget install Nvidia.CUDA>&2 +exit /b 1 + +:failed +echo.>&2 +echo FAILED: nvcc exited with %RC%.>&2 +echo.>&2 +echo "unsupported gpu architecture" - CUDA older than the target.>&2 +echo sm_120 needs CUDA 12.8+. Override: set CUDA_ARCH=sm_89>&2 +echo.>&2 +echo "unsupported Microsoft Visual Studio version" - your VS is NEWER than>&2 +echo CUDA supports (it accepts 2017-2022 only). Install the 2022 toolset>&2 +echo side by side; it coexists with newer VS and this script prefers it:>&2 +echo winget install Microsoft.VisualStudio.2022.BuildTools>&2 +echo (then add the "Desktop development with C++" workload)>&2 +echo Or point at a specific cl.exe directory:>&2 +echo set CCBIN=C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Tools\MSVC\\bin\Hostx64\x64>&2 +exit /b %RC% From fbdb1099872fc30d1794c3fbee9ba90c9409e98e Mon Sep 17 00:00:00 2001 From: Egon Ruiter Date: Thu, 23 Jul 2026 20:15:48 +0200 Subject: [PATCH 39/96] docs/tools: document PILOT_EVICT_GUARD and add CLI overrides to test_olmoe_real --- c/olmoe.c | 1 + c/tools/test_olmoe_real.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/c/olmoe.c b/c/olmoe.c index 74df155b..a1cc0495 100644 --- a/c/olmoe.c +++ b/c/olmoe.c @@ -13,6 +13,7 @@ * WIDE=N : prefetch top-K*N candidates (default 1, try 2 or 3) * SMOOTH=F : EMA coefficient for routing momentum (default 0.3, range 0.0-0.95) * CONF_LIMIT=F : cumulative gate probability threshold for prefetch cutoff (default 0.92) + * PILOT_EVICT_GUARD=0/1 : 1=enable LFRU prefetch eviction guard (default), 0=disable * (expert queue is sorted by eid for SSD read locality) */ #define _GNU_SOURCE diff --git a/c/tools/test_olmoe_real.py b/c/tools/test_olmoe_real.py index 1abf64d0..384b2f9d 100644 --- a/c/tools/test_olmoe_real.py +++ b/c/tools/test_olmoe_real.py @@ -30,8 +30,8 @@ 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) +CACHE_SIZE = int(sys.argv[1]) if len(sys.argv) > 1 else 32 # experts cached per layer +QUANT_BITS = int(sys.argv[2]) if len(sys.argv) > 2 else 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). From a391eeefa0b44596b828eb5ce1ff92fcab9972dc Mon Sep 17 00:00:00 2001 From: JustVugg Date: Thu, 23 Jul 2026 20:32:31 +0200 Subject: [PATCH 40/96] serve: COLI_SERVE_ALL_STOPS re-arms the full stop set (#549) Some quantized containers (notably int4-gs64) end a turn on an end-of-turn special OTHER than <|endoftext|>. Serve mode arms only <|endoftext|> (#401 tool-call safety), so on those containers the model never stops and runs into hallucinated turns. COLI_SERVE_ALL_STOPS=1 re-arms every stop token for users not doing tool calls (or whose client owns the tool boundary). Default off: the #401 filter is unchanged. Diagnosis by @ZacharyZcR on the issue. Co-Authored-By: Claude Fable 5 --- c/sample.h | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/c/sample.h b/c/sample.h index 414c6210..1df00ad4 100644 --- a/c/sample.h +++ b/c/sample.h @@ -119,8 +119,14 @@ static void stops_arm_tok(const Cfg *c, int tok_eos, Tok *T){ /* #401: in serve mode keep ONLY <|endoftext|>. Role markers <|user|>/<|observation|> * (config stops + tokenizer special set) are boundaries the Python server owns; as * hard stops they cut generation the moment the model opens a block, - * because int4 argmax noise picks a stop-token ID over the correct '<' token. */ - if (getenv("SERVE") && tok_eos >= 0) { + * because int4 argmax noise picks a stop-token ID over the correct '<' token. + * + * #549: on some quantized containers (notably int4-gs64) an end-of-turn special + * OTHER than <|endoftext|> wins the final-token margin, so serve mode never stops and + * runs into hallucinated turns. COLI_SERVE_ALL_STOPS=1 re-arms the full stop set for + * users who are NOT doing tool calls (or whose client owns the tool boundary), at the + * cost of the #401 tool-call safety. Default off — filter unchanged. */ + if (getenv("SERVE") && tok_eos >= 0 && !getenv("COLI_SERVE_ALL_STOPS")) { int kept = 0; for (int i = 0; i < g_nstop; i++) if (g_stop[i] == tok_eos) g_stop[kept++] = g_stop[i]; if (kept < g_nstop) fprintf(stderr, "[stop] serve mode: filtered %d non-EOS stop tokens (tool-call safety, #401)\n", g_nstop - kept); From cc5df232f763887fd8ce07a15c18c22aa71dd0bc Mon Sep 17 00:00:00 2001 From: Bill Herald <641463+bherald@users.noreply.github.com> Date: Wed, 22 Jul 2026 16:28:31 -0400 Subject: [PATCH 41/96] feat: support OpenAI stop sequences --- c/colibri.c | 10 ++++ c/openai_server.py | 107 +++++++++++++++++++++++++++++----- c/tests/test_openai_server.py | 100 +++++++++++++++++++++++++++---- docs/api.md | 8 ++- docs/serve_protocol.md | 5 ++ 5 files changed, 203 insertions(+), 27 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 82eeac21..03d08b32 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -5471,6 +5471,16 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g char *line=NULL; size_t cap=0; ssize_t nr=getline(&line,&cap,stdin); if(nr<0){ free(line); return -1; } if(nr && line[nr-1]=='\n') line[--nr]=0; + if(!strncmp(line,"STOP ",5)){ + unsigned long long id=0; char tail; + if(sscanf(line+5,"%llu %c",&id,&tail)!=1 || id==0){ + printf("ERROR 0 BAD_REQUEST\n"); fflush(stdout); free(line); return 0; + } + for(int i=0;i= 0 and (match is None or candidate[:2] < match[:2]): + match = candidate + if match is not None: + offset, _order, self.matched = match + if offset: + self.emit(text[:offset]) + self.pending = "" + return + + hold = 0 + maximum = min(len(text), max((len(s) - 1 for s in self.sequences), default=0)) + for size in range(1, maximum + 1): + suffix = text[-size:] + if any(sequence.startswith(suffix) for sequence in self.sequences): + hold = size + flush = len(text) - hold + if flush: + self.emit(text[:flush]) + self.pending = text[flush:] + + def finish(self): + if self.matched is None and self.pending: + self.emit(self.pending) + self.pending = "" + + def stopped(self): + return self.matched is not None + def generation_options(body, limit): if body.get("n", 1) != 1: raise APIError(400, "Colibri currently supports `n=1` only.", "n", "unsupported_value") @@ -878,8 +944,7 @@ def generation_options(body, limit): "tool_choice", "invalid_value") if choice != "none" and not (body.get("tools") or body.get("functions")): raise APIError(400, "`tool_choice` requires `tools`.", "tool_choice", "invalid_value") - if body.get("stop") is not None: - raise APIError(400, "Custom stop sequences are not supported yet.", "stop", "unsupported_parameter") + stop_sequences = parse_stop_sequences(body) if body.get("logprobs"): raise APIError(400, "Log probabilities are not supported yet.", "logprobs", "unsupported_parameter") if body.get("frequency_penalty", 0) or body.get("presence_penalty", 0): @@ -944,7 +1009,7 @@ def generation_options(body, limit): if (isinstance(top_p, bool) or not isinstance(top_p, (int, float)) or not math.isfinite(top_p) or not 0 < top_p <= 1): raise APIError(400, "`top_p` must be greater than 0 and at most 1.", "top_p") - return maximum, float(temperature), float(top_p), grammar + return maximum, float(temperature), float(top_p), grammar, stop_sequences def read_engine_turn(stream, sentinel, on_bytes): @@ -1108,7 +1173,7 @@ def _dispatch_stdout(self): self._fail_pending(error) def generate(self, prompt, max_tokens, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None): + cancelled=None, grammar=None, stopped=None): if isinstance(cache_slot, bool) or not isinstance(cache_slot, int) or not 0 <= cache_slot < self.kv_slots: raise APIError(400, "Invalid cache slot.", "cache_slot") payload = prompt.encode("utf-8") @@ -1150,12 +1215,18 @@ def decode(data): raise cancel_sent = False + stop_sent = False while True: kind, value = events.get() if kind == "data": - if not cancel_sent: + if not cancel_sent and not stop_sent: decode(value) - if cancelled and cancelled(): + if stopped and stopped(): + stop_sent = True + with self.write_lock: + self.process.stdin.write(f"STOP {request_id}\n".encode()) + self.process.stdin.flush() + elif cancelled and cancelled(): cancel_sent = True with self.write_lock: self.process.stdin.write(f"CANCEL {request_id}\n".encode()) @@ -1443,7 +1514,8 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non if dbg >= 2: sys.stderr.write(f"\n===== PROMPT [{request_id}] =====\n{prompt}\n===== OUTPUT [{request_id}] =====\n") sys.stderr.flush() - maximum, temperature, top_p, grammar = generation_options(body, self.server.max_tokens) + maximum, temperature, top_p, grammar, stop_sequences = generation_options( + body, self.server.max_tokens) if grammar is not None and ARCH == "inkling": # inkling.c's serve loop speaks the 6-field SUBMIT header only; sending the # grammar payload extension would desync its stdin framing. @@ -1475,9 +1547,11 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non queue_headers = {"x-colibri-queue-wait-ms": str(round(queue_wait * 1000))} if not stream: output = [] + stop_filter = StopFilter(stop_sequences, output.append) stats = self.server.engine.generate( - prompt, maximum, temperature, top_p, output.append, cache_slot, - self.client_disconnected, grammar=grammar) + prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, + self.client_disconnected, grammar=grammar, stopped=stop_filter.stopped) + stop_filter.finish() text = "".join(output) reasoning = "" if ARCH == "inkling": @@ -1597,9 +1671,11 @@ def emit_tools(chunk): if flush: emit(sp["buf"][:flush]) sp["buf"] = sp["buf"][flush:] + stop_filter = StopFilter(stop_sequences, emit_tools) stats = self.server.engine.generate( - prompt, maximum, temperature, top_p, emit_tools, cache_slot, - lambda: not connected, grammar=grammar) + prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, + lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) + stop_filter.finish() if not sp["tool"] and sp["buf"]: emit(sp["buf"]) # no tool call happened: flush held tail _content, calls = parse_tool_calls("".join(raw), tools) @@ -1614,9 +1690,11 @@ def emit_plain(chunk): if dbg_echo: sys.stderr.write(chunk); sys.stderr.flush() (splitter.feed if splitter else emit)(chunk) + stop_filter = StopFilter(stop_sequences, emit_plain) stats = self.server.engine.generate( - prompt, maximum, temperature, top_p, emit_plain, cache_slot, - lambda: not connected, grammar=grammar) + prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, + lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) + stop_filter.finish() if splitter: splitter.close() finish = "length" if stats["length_limited"] else "stop" @@ -1710,7 +1788,8 @@ def anthropic_messages(self, body, request_id): self.anthropic_generation(translated, prompt, request_id, tools, enable_thinking) def anthropic_generation(self, body, prompt, request_id, tools, enable_thinking): - maximum, temperature, top_p, grammar = generation_options(body, self.server.max_tokens) + maximum, temperature, top_p, grammar, _stop_sequences = generation_options( + body, self.server.max_tokens) cache_slot = body.get("cache_slot") if (cache_slot is not None and (isinstance(cache_slot, bool) or not isinstance(cache_slot, int) or diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index 98da4c18..24e6f47e 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -11,19 +11,23 @@ from pathlib import Path from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, END, GenerationScheduler, - READY, Engine, _engine_error, generation_options, parse_tool_calls, - read_engine_turn, render_chat, serve) + READY, Engine, StopFilter, _engine_error, generation_options, + parse_tool_calls, read_engine_turn, render_chat, serve) class FakeEngine: def __init__(self): self.calls = [] + self.stop_requests = 0 def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None): + cancelled=None, grammar=None, stopped=None): self.calls.append((prompt, maximum, temperature, top_p, cache_slot, grammar)) - on_text("Hé") - on_text("llo") + for chunk in ("Hé", "llo"): + on_text(chunk) + if stopped and stopped(): + self.stop_requests += 1 + break return {"prompt_tokens": 7, "completion_tokens": 2, "length_limited": False} @@ -34,11 +38,11 @@ def __init__(self): self.release = threading.Event() def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None): + cancelled=None, grammar=None, stopped=None): self.entered.set() self.release.wait(2) return super().generate(prompt, maximum, temperature, top_p, on_text, cache_slot, - cancelled) + cancelled, grammar, stopped) class TemplateTest(unittest.TestCase): @@ -71,11 +75,11 @@ def test_renders_thinking_prefix(self): def test_validates_generation_limits(self): self.assertEqual(generation_options({"max_tokens": 4, "temperature": 0, "top_p": 1}, 8), - (4, 0.0, 1.0, None)) + (4, 0.0, 1.0, None, ())) # max_tokens above the server cap is clamped, not rejected (#260): OpenAI # clients default to large values; erroring breaks them. self.assertEqual(generation_options({"max_tokens": 9, "temperature": 0, "top_p": 1}, 8), - (8, 0.0, 1.0, None)) + (8, 0.0, 1.0, None, ())) # non-positive / non-int max_tokens is still a hard error with self.assertRaises(APIError): generation_options({"max_tokens": 0}, 8) @@ -84,7 +88,7 @@ def test_validates_generation_limits(self): with self.assertRaises(APIError): generation_options({"top_p": math.inf}, 8) self.assertEqual(generation_options({"temperature": None, "top_p": None}, 8), - (8, 0.7, 0.9, None)) + (8, 0.7, 0.9, None, ())) # response_format -> grammar plumbing (draft source, never a constraint) opts = generation_options({"max_tokens": 4, "response_format": {"type": "json_object"}}, 8) self.assertIn("root ::=", opts[3]) @@ -110,6 +114,32 @@ def test_validates_generation_limits(self): opts = generation_options({"response_format": {"type": "gbnf", "grammar": "not a grammar ::="}}, 8) self.assertEqual(opts[3], "not a grammar ::=") + def test_validates_stop_sequences(self): + self.assertEqual(generation_options({"stop": "END"}, 8)[4], ("END",)) + self.assertEqual(generation_options({"stop": ["ONE", "TWO"]}, 8)[4], + ("ONE", "TWO")) + for value in ("", [], [""], ["1", "2", "3", "4", "5"], 7, ["ok", 7]): + with self.subTest(value=value), self.assertRaises(APIError): + generation_options({"stop": value}, 8) + + +class StopFilterTest(unittest.TestCase): + def test_hides_match_split_across_chunks(self): + output = [] + stop_filter = StopFilter(("STOP",), output.append) + for chunk in ("answer S", "TO", "Pignored"): + stop_filter.feed(chunk) + stop_filter.finish() + self.assertEqual("".join(output), "answer ") + self.assertEqual(stop_filter.matched, "STOP") + + def test_flushes_partial_prefix_when_generation_finishes(self): + output = [] + stop_filter = StopFilter(("STOP",), output.append) + stop_filter.feed("answer ST") + stop_filter.finish() + self.assertEqual("".join(output), "answer ST") + class ProtocolTest(unittest.TestCase): def test_reads_payload_and_extended_status(self): @@ -446,6 +476,30 @@ def respond(process, frame): self.assertEqual(output, ["x"]) self.assertEqual(process.writes[-1].split(), [b"CANCEL", request_id]) + def test_stops_generation_through_successful_done_path(self): + request_id = None + + def respond(process, frame): + nonlocal request_id + fields = frame.split() + if fields[0] == b"SUBMIT": + request_id = fields[1] + process.stdout.feed(b"DATA " + request_id + b" 1\nx\n") + elif fields[0] == b"STOP": + self.assertEqual(fields[1], request_id) + process.stdout.feed(b"DONE " + request_id + b" STAT 1 1 0 1 2 0\n") + + process = FakeProcess(respond) + with patch("openai_server.subprocess.Popen", return_value=process): + engine = Engine("glm", "model") + output = [] + stats = engine.generate("hello", 8, 0.7, 0.9, output.append, + stopped=lambda: output == ["x"]) + engine.close() + self.assertEqual(output, ["x"]) + self.assertEqual(stats["completion_tokens"], 1) + self.assertEqual(process.writes[-1].split(), [b"STOP", request_id]) + class HTTPTest(unittest.TestCase): @classmethod @@ -525,6 +579,17 @@ def test_chat_completion(self): self.assertIn("<|user|>Hi<|assistant|>", self.engine.calls[-1][0]) self.assertEqual(self.engine.calls[-1][4], 1) + def test_chat_completion_stops_across_engine_chunks(self): + before = self.engine.stop_requests + with self.request("/v1/chat/completions", { + "model": "test-model", "messages": [{"role": "user", "content": "Hi"}], + "stop": "éll", + }) as response: + body = json.load(response) + self.assertEqual(body["choices"][0]["message"]["content"], "H") + self.assertEqual(body["choices"][0]["finish_reason"], "stop") + self.assertEqual(self.engine.stop_requests, before + 1) + def test_rejects_invalid_cache_slot(self): with self.assertRaises(HTTPError) as caught: self.request("/v1/chat/completions", { @@ -545,6 +610,21 @@ def test_streaming_chat_completion(self): self.assertIn('\"usage\":{\"prompt_tokens\":7,\"completion_tokens\":2,\"total_tokens\":9}', stream) self.assertTrue(stream.endswith("data: [DONE]\n\n")) + def test_streaming_stop_never_exposes_partial_sequence(self): + before = self.engine.stop_requests + with self.request("/v1/chat/completions", { + "model": "test-model", "messages": [{"role": "user", "content": "Hi"}], + "stream": True, "stop": "éll", + }) as response: + raw = response.read().decode() + payloads = [json.loads(line[6:]) for line in raw.splitlines() + if line.startswith("data: ") and line != "data: [DONE]"] + content = "".join((choice.get("delta") or {}).get("content", "") + for payload in payloads for choice in payload["choices"]) + self.assertEqual(content, "H") + self.assertEqual(payloads[-1]["choices"][0]["finish_reason"], "stop") + self.assertEqual(self.engine.stop_requests, before + 1) + def test_legacy_completion(self): with self.request("/v1/completions", { "model": "test-model", "prompt": "Complete me", "temperature": 0, diff --git a/docs/api.md b/docs/api.md index 2b92bb6e..f3bb16c3 100644 --- a/docs/api.md +++ b/docs/api.md @@ -24,14 +24,16 @@ curl http://127.0.0.1:8000/v1/chat/completions \ Implemented endpoints are `GET /v1/models`, `GET /v1/models/{model}`, `POST /v1/chat/completions`, and legacy `POST /v1/completions`. Chat and completion requests support JSON responses, SSE streaming, usage counts, -`max_tokens`/`max_completion_tokens`, `temperature`, and `top_p`. The extension +`max_tokens`/`max_completion_tokens`, `temperature`, `top_p`, and up to four +custom `stop` sequences. Stop sequences are removed from the response and end +generation early in both JSON and streaming modes. The extension `enable_thinking: true` enables GLM-5.2's reasoning block; the standard `reasoning_effort` field also enables it unless set to `none`. The server is deliberately text-only and serves one generation at a time: the 744B model stays in one persistent process, so concurrent HTTP requests queue -instead of loading duplicate model copies. Tools, image/audio input, custom -stop sequences, log probabilities, and token penalties return an explicit error +instead of loading duplicate model copies. Image/audio input, log probabilities, +and token penalties return an explicit error rather than being silently ignored. The default bind address is localhost; set `COLI_API_KEY` before exposing the server beyond the machine. diff --git a/docs/serve_protocol.md b/docs/serve_protocol.md index b0eb61ec..8b9000b4 100644 --- a/docs/serve_protocol.md +++ b/docs/serve_protocol.md @@ -33,6 +33,7 @@ recognize**; that is the protocol's forward-compatibility rule. ``` SUBMIT \n\n +STOP \n CANCEL \n ``` @@ -44,6 +45,10 @@ CANCEL \n - `bytes` — exact byte length of `payload` (UTF-8, may contain newlines). The engine reads exactly that many bytes after the header line, then one trailing `\n`. - `payload` — the fully rendered prompt (the server owns the chat template). +- `STOP` ends generation through the normal successful `DONE` path. Statistics, + usage history, and KV state are persisted; the HTTP gateway uses it after a + client-provided stop sequence matches. +- `CANCEL` aborts a request after its client disconnects and returns `CANCELLED`. - EOF on stdin = graceful shutdown: in-flight requests finish first. Prefill is serial; decode is continuously batched — every active slot contributes From 37c8fac9b5bdbc64eda71e927ba049e53a442966 Mon Sep 17 00:00:00 2001 From: Bill Herald <641463+bherald@users.noreply.github.com> Date: Wed, 22 Jul 2026 17:35:51 -0400 Subject: [PATCH 42/96] feat: support patient native stop handling --- c/openai_server.py | 57 ++++++++++++++++++++++++----------- c/tests/test_openai_server.py | 44 +++++++++++++++++++++++++++ docs/api.md | 4 +++ 3 files changed, 88 insertions(+), 17 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index aa50986a..413ab2e0 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -856,27 +856,46 @@ def parse_stop_sequences(body): class StopFilter: """Stream text without exposing a full or partial stop sequence.""" - def __init__(self, sequences, emit): + def __init__(self, sequences, emit, ignore_leading=False): self.sequences = tuple(sequences) self.emit = emit + self.ignore_leading = ignore_leading self.pending = "" self.matched = None + self.useful_content_seen = False + self.leading_matches_ignored = 0 + + def _emit(self, text): + if text: + self.emit(text) + if text.strip(): + self.useful_content_seen = True def feed(self, chunk): if self.matched is not None: return text = self.pending + chunk - match = None - for order, sequence in enumerate(self.sequences): - offset = text.find(sequence) - candidate = (offset, order, sequence) - if offset >= 0 and (match is None or candidate[:2] < match[:2]): - match = candidate - if match is not None: - offset, _order, self.matched = match - if offset: - self.emit(text[:offset]) - self.pending = "" + self.pending = "" + while True: + match = None + for order, sequence in enumerate(self.sequences): + offset = text.find(sequence) + candidate = (offset, order, sequence) + if offset >= 0 and (match is None or candidate[:2] < match[:2]): + match = candidate + if match is None: + break + offset, _order, sequence = match + prefix = text[:offset] + if (self.ignore_leading and not self.useful_content_seen + and not prefix.strip()): + self.leading_matches_ignored += 1 + text = text[offset + len(sequence):] + if not text: + return + continue + self.matched = sequence + self._emit(prefix) return hold = 0 @@ -887,12 +906,12 @@ def feed(self, chunk): hold = size flush = len(text) - hold if flush: - self.emit(text[:flush]) + self._emit(text[:flush]) self.pending = text[flush:] def finish(self): if self.matched is None and self.pending: - self.emit(self.pending) + self._emit(self.pending) self.pending = "" def stopped(self): @@ -1521,6 +1540,10 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non # grammar payload extension would desync its stdin framing. raise APIError(400, "`response_format` grammars are not supported by the Inkling " "engine yet.", "response_format", "unsupported_parameter") + ignore_leading_stop = body.get("x_colibri_ignore_leading_stop", False) + if not isinstance(ignore_leading_stop, bool): + raise APIError(400, "`x_colibri_ignore_leading_stop` must be a boolean.", + "x_colibri_ignore_leading_stop", "invalid_value") # tools and tool_choice come from chat_completion() already processed/filtered if chat and tool_choice == "none": tools = None # client forbade tools: never surface tool_calls @@ -1547,7 +1570,7 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non queue_headers = {"x-colibri-queue-wait-ms": str(round(queue_wait * 1000))} if not stream: output = [] - stop_filter = StopFilter(stop_sequences, output.append) + stop_filter = StopFilter(stop_sequences, output.append, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, self.client_disconnected, grammar=grammar, stopped=stop_filter.stopped) @@ -1671,7 +1694,7 @@ def emit_tools(chunk): if flush: emit(sp["buf"][:flush]) sp["buf"] = sp["buf"][flush:] - stop_filter = StopFilter(stop_sequences, emit_tools) + stop_filter = StopFilter(stop_sequences, emit_tools, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) @@ -1690,7 +1713,7 @@ def emit_plain(chunk): if dbg_echo: sys.stderr.write(chunk); sys.stderr.flush() (splitter.feed if splitter else emit)(chunk) - stop_filter = StopFilter(stop_sequences, emit_plain) + stop_filter = StopFilter(stop_sequences, emit_plain, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index 24e6f47e..eb40ce44 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -140,6 +140,32 @@ def test_flushes_partial_prefix_when_generation_finishes(self): stop_filter.finish() self.assertEqual("".join(output), "answer ST") + def test_optional_patient_mode_ignores_only_leading_matches(self): + output = [] + stop_filter = StopFilter(("<|user|>",), output.append, ignore_leading=True) + for chunk in ("<|us", "er|>answer", "<|user|>ignored"): + stop_filter.feed(chunk) + stop_filter.finish() + self.assertEqual("".join(output), "answer") + self.assertEqual(stop_filter.matched, "<|user|>") + self.assertEqual(stop_filter.leading_matches_ignored, 1) + + def test_patient_mode_preserves_remainder_after_same_chunk_leading_match(self): + output = [] + stop_filter = StopFilter(("STOP",), output.append, ignore_leading=True) + stop_filter.feed("STOPuseful STOPdiscarded") + stop_filter.finish() + self.assertEqual("".join(output), "useful ") + self.assertEqual(stop_filter.matched, "STOP") + + def test_strict_mode_still_stops_on_a_leading_match(self): + output = [] + stop_filter = StopFilter(("STOP",), output.append) + stop_filter.feed("STOPignored") + stop_filter.finish() + self.assertEqual(output, []) + self.assertEqual(stop_filter.matched, "STOP") + class ProtocolTest(unittest.TestCase): def test_reads_payload_and_extended_status(self): @@ -590,6 +616,24 @@ def test_chat_completion_stops_across_engine_chunks(self): self.assertEqual(body["choices"][0]["finish_reason"], "stop") self.assertEqual(self.engine.stop_requests, before + 1) + def test_patient_stop_extension_ignores_a_leading_match(self): + before = self.engine.stop_requests + with self.request("/v1/chat/completions", { + "model": "test-model", "messages": [{"role": "user", "content": "Hi"}], + "stop": "H", "x_colibri_ignore_leading_stop": True, + }) as response: + body = json.load(response) + self.assertEqual(body["choices"][0]["message"]["content"], "éllo") + self.assertEqual(self.engine.stop_requests, before) + + def test_patient_stop_extension_requires_a_boolean(self): + with self.assertRaises(HTTPError) as caught: + self.request("/v1/chat/completions", { + "model": "test-model", "messages": [{"role": "user", "content": "Hi"}], + "stop": "H", "x_colibri_ignore_leading_stop": "yes", + }) + self.assertEqual(caught.exception.code, 400) + def test_rejects_invalid_cache_slot(self): with self.assertRaises(HTTPError) as caught: self.request("/v1/chat/completions", { diff --git a/docs/api.md b/docs/api.md index f3bb16c3..80ecbbe7 100644 --- a/docs/api.md +++ b/docs/api.md @@ -27,6 +27,10 @@ completion requests support JSON responses, SSE streaming, usage counts, `max_tokens`/`max_completion_tokens`, `temperature`, `top_p`, and up to four custom `stop` sequences. Stop sequences are removed from the response and end generation early in both JSON and streaming modes. The extension +`x_colibri_ignore_leading_stop: true` discards leading stop sequences until +the first non-whitespace response content, which is useful for local templates +that occasionally emit a role marker before the answer; strict OpenAI stop +behavior remains the default. The extension `enable_thinking: true` enables GLM-5.2's reasoning block; the standard `reasoning_effort` field also enables it unless set to `none`. From 83fc0a71b8cb2d3a128b8b4d47ab48731b05c247 Mon Sep 17 00:00:00 2001 From: bill Date: Thu, 23 Jul 2026 06:41:21 -0400 Subject: [PATCH 43/96] fix: stop chat at implicit role boundaries --- c/openai_server.py | 24 +++++++++++++++++++----- c/tests/test_openai_server.py | 15 +++++++++++++-- docs/api.md | 6 +++++- 3 files changed, 37 insertions(+), 8 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 413ab2e0..fa336fcd 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -832,6 +832,8 @@ def anthropic_tools(body): 'jws ::= ( " " | "\\t" | "\\n" | "\\r" )*\n' ) +DEFAULT_CHAT_STOP_SEQUENCES = ("<|user|>", "<|observation|>") + def parse_stop_sequences(body): value = body.get("stop") @@ -854,6 +856,21 @@ def parse_stop_sequences(body): return tuple(sequences) +def stop_policy(body, chat): + sequences = parse_stop_sequences(body) + ignore_leading = body.get("x_colibri_ignore_leading_stop", False) + if not isinstance(ignore_leading, bool): + raise APIError(400, "`x_colibri_ignore_leading_stop` must be a boolean.", + "x_colibri_ignore_leading_stop", "invalid_value") + if chat and not sequences: + # The chat template owns these role boundaries, so generic OpenAI + # clients should not need model-specific stop knowledge. Treat an + # occasional leading marker patiently; client-provided stops remain + # strict unless the extension is explicitly requested. + return DEFAULT_CHAT_STOP_SEQUENCES, True + return sequences, ignore_leading + + class StopFilter: """Stream text without exposing a full or partial stop sequence.""" def __init__(self, sequences, emit, ignore_leading=False): @@ -1533,17 +1550,14 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non if dbg >= 2: sys.stderr.write(f"\n===== PROMPT [{request_id}] =====\n{prompt}\n===== OUTPUT [{request_id}] =====\n") sys.stderr.flush() - maximum, temperature, top_p, grammar, stop_sequences = generation_options( + maximum, temperature, top_p, grammar, _requested_stop_sequences = generation_options( body, self.server.max_tokens) if grammar is not None and ARCH == "inkling": # inkling.c's serve loop speaks the 6-field SUBMIT header only; sending the # grammar payload extension would desync its stdin framing. raise APIError(400, "`response_format` grammars are not supported by the Inkling " "engine yet.", "response_format", "unsupported_parameter") - ignore_leading_stop = body.get("x_colibri_ignore_leading_stop", False) - if not isinstance(ignore_leading_stop, bool): - raise APIError(400, "`x_colibri_ignore_leading_stop` must be a boolean.", - "x_colibri_ignore_leading_stop", "invalid_value") + stop_sequences, ignore_leading_stop = stop_policy(body, chat) # tools and tool_choice come from chat_completion() already processed/filtered if chat and tool_choice == "none": tools = None # client forbade tools: never surface tool_calls diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index eb40ce44..c3c999d1 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -10,9 +10,10 @@ from urllib.request import Request, urlopen from pathlib import Path -from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, END, GenerationScheduler, +from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, + DEFAULT_CHAT_STOP_SEQUENCES, END, GenerationScheduler, READY, Engine, StopFilter, _engine_error, generation_options, - parse_tool_calls, read_engine_turn, render_chat, serve) + parse_tool_calls, read_engine_turn, render_chat, serve, stop_policy) class FakeEngine: @@ -122,6 +123,16 @@ def test_validates_stop_sequences(self): with self.subTest(value=value), self.assertRaises(APIError): generation_options({"stop": value}, 8) + def test_chat_defaults_role_stops_without_changing_client_or_completion_policy(self): + self.assertEqual(stop_policy({}, True), (DEFAULT_CHAT_STOP_SEQUENCES, True)) + self.assertEqual(stop_policy({}, False), ((), False)) + self.assertEqual(stop_policy({"stop": "END"}, True), (("END",), False)) + self.assertEqual(stop_policy({ + "stop": "END", "x_colibri_ignore_leading_stop": True, + }, True), (("END",), True)) + with self.assertRaises(APIError): + stop_policy({"x_colibri_ignore_leading_stop": "yes"}, True) + class StopFilterTest(unittest.TestCase): def test_hides_match_split_across_chunks(self): diff --git a/docs/api.md b/docs/api.md index 80ecbbe7..8522f3be 100644 --- a/docs/api.md +++ b/docs/api.md @@ -30,7 +30,11 @@ generation early in both JSON and streaming modes. The extension `x_colibri_ignore_leading_stop: true` discards leading stop sequences until the first non-whitespace response content, which is useful for local templates that occasionally emit a role marker before the answer; strict OpenAI stop -behavior remains the default. The extension +behavior remains the default for client-provided sequences. Chat requests with +no client `stop` automatically use the template's `<|user|>` and +`<|observation|>` role markers, patiently ignoring only leading markers; this +prevents a model-completed turn from silently generating a new user or tool +turn. Legacy completion requests receive no implicit stop sequences. The extension `enable_thinking: true` enables GLM-5.2's reasoning block; the standard `reasoning_effort` field also enables it unless set to `none`. From 74326b9d9182a0c69e7423586bbfe75f60aa48d7 Mon Sep 17 00:00:00 2001 From: bill Date: Thu, 23 Jul 2026 16:41:14 -0400 Subject: [PATCH 44/96] fix: scope implicit stops to GLM chat --- c/openai_server.py | 9 +++++---- c/tests/test_openai_server.py | 36 ++++++++++++++++++++++++++--------- docs/api.md | 7 ++++--- 3 files changed, 36 insertions(+), 16 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index fa336fcd..0080bd73 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -862,10 +862,11 @@ def stop_policy(body, chat): if not isinstance(ignore_leading, bool): raise APIError(400, "`x_colibri_ignore_leading_stop` must be a boolean.", "x_colibri_ignore_leading_stop", "invalid_value") - if chat and not sequences: - # The chat template owns these role boundaries, so generic OpenAI - # clients should not need model-specific stop knowledge. Treat an - # occasional leading marker patiently; client-provided stops remain + if chat and ARCH == "glm" and not sequences: + # The GLM chat template owns these role boundaries, so generic OpenAI + # clients should not need model-specific stop knowledge. Inkling has a + # different marker family and receives no implicit GLM stops. Treat an + # occasional leading GLM marker patiently; client-provided stops remain # strict unless the extension is explicitly requested. return DEFAULT_CHAT_STOP_SEQUENCES, True return sequences, ignore_leading diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index c3c999d1..a9a31e69 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -12,8 +12,9 @@ from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, DEFAULT_CHAT_STOP_SEQUENCES, END, GenerationScheduler, - READY, Engine, StopFilter, _engine_error, generation_options, - parse_tool_calls, read_engine_turn, render_chat, serve, stop_policy) + READY, Engine, InklingStreamSplit, StopFilter, _engine_error, + generation_options, parse_tool_calls, read_engine_turn, render_chat, + serve, stop_policy) class FakeEngine: @@ -123,18 +124,35 @@ def test_validates_stop_sequences(self): with self.subTest(value=value), self.assertRaises(APIError): generation_options({"stop": value}, 8) - def test_chat_defaults_role_stops_without_changing_client_or_completion_policy(self): - self.assertEqual(stop_policy({}, True), (DEFAULT_CHAT_STOP_SEQUENCES, True)) - self.assertEqual(stop_policy({}, False), ((), False)) - self.assertEqual(stop_policy({"stop": "END"}, True), (("END",), False)) - self.assertEqual(stop_policy({ - "stop": "END", "x_colibri_ignore_leading_stop": True, - }, True), (("END",), True)) + def test_glm_chat_defaults_role_stops_without_changing_other_policies(self): + with patch("openai_server.ARCH", "glm"): + self.assertEqual(stop_policy({}, True), (DEFAULT_CHAT_STOP_SEQUENCES, True)) + self.assertEqual(stop_policy({}, False), ((), False)) + self.assertEqual(stop_policy({"stop": "END"}, True), (("END",), False)) + self.assertEqual(stop_policy({ + "stop": "END", "x_colibri_ignore_leading_stop": True, + }, True), (("END",), True)) + with patch("openai_server.ARCH", "inkling"): + self.assertEqual(stop_policy({}, True), ((), False)) + self.assertEqual(stop_policy({"stop": "END"}, True), (("END",), False)) with self.assertRaises(APIError): stop_policy({"x_colibri_ignore_leading_stop": "yes"}, True) class StopFilterTest(unittest.TestCase): + def test_explicit_stop_composes_with_inkling_stream_split(self): + content = [] + reasoning = [] + splitter = InklingStreamSplit(content.append, reasoning.append) + stop_filter = StopFilter(("END",), splitter.feed) + for chunk in ("<|content_thinking|>why<|content_text|>answer EN", "Dignored"): + stop_filter.feed(chunk) + stop_filter.finish() + splitter.close() + self.assertEqual("".join(reasoning), "why") + self.assertEqual("".join(content), "answer ") + self.assertEqual(stop_filter.matched, "END") + def test_hides_match_split_across_chunks(self): output = [] stop_filter = StopFilter(("STOP",), output.append) diff --git a/docs/api.md b/docs/api.md index 8522f3be..3f5f86b8 100644 --- a/docs/api.md +++ b/docs/api.md @@ -30,11 +30,12 @@ generation early in both JSON and streaming modes. The extension `x_colibri_ignore_leading_stop: true` discards leading stop sequences until the first non-whitespace response content, which is useful for local templates that occasionally emit a role marker before the answer; strict OpenAI stop -behavior remains the default for client-provided sequences. Chat requests with -no client `stop` automatically use the template's `<|user|>` and +behavior remains the default for client-provided sequences. GLM chat requests +with no client `stop` automatically use the template's `<|user|>` and `<|observation|>` role markers, patiently ignoring only leading markers; this prevents a model-completed turn from silently generating a new user or tool -turn. Legacy completion requests receive no implicit stop sequences. The extension +turn. Inkling chat and legacy completion requests receive no implicit GLM stop +sequences. The extension `enable_thinking: true` enables GLM-5.2's reasoning block; the standard `reasoning_effort` field also enables it unless set to `none`. From db4ed612f650665d8943b8eb79905f4998482d50 Mon Sep 17 00:00:00 2001 From: recviking Date: Thu, 23 Jul 2026 19:48:29 -0400 Subject: [PATCH 45/96] serve: emit finish_reason=length on real cap-hits, not just at admission r->length_limited was set at admission time whenever the requested max_tokens exceeded KV context room, even when the request went on to finish naturally via EOS -- and was never set at any of the three sites where a request is actually cut off because emitted reached maximum (mux_submit's single-token path, the grammar-forced-draft verify loop, and the plain shared-batch decode loop). Since length_limited is the sole source openai_server.py uses for finish_reason ("length" if set else "stop"), naturally-completed generations could be mislabeled "length" and genuine cap-hits could be mislabeled "stop". Traced end to end against #455's KV-resume repro: coli serve always runs through run_serve_mux (SERVE_BATCH=1 is hardcoded in openai_server.py's Engine.__init__), and that repro's plain-chat request (no tool/grammar mode) hits the shared-batch decode loop's unflagged cap-hit exactly. This fixes that side-bug specifically; it does not address #455's root cause (per-row-scale int4 quantization, already resolved upstream via a gs64 container) or its loop/repetition protection ask, both out of scope here. No perf impact -- this only changes which value gets written to ServeReq.length_limited, consumed solely by the STAT line's last field. Co-Authored-By: Claude Sonnet 5 --- c/colibri.c | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 82eeac21..5856765c 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -5594,14 +5594,21 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g r->id=sub.id; r->maximum=sub.max_tokens; r->temp=sub.temperature; r->top_p=sub.top_p; r->prompt_tokens=nt; r->started=now_s(); r->hits0=m->hits; r->miss0=m->miss; prof_base(m,&r->pb); /* a few loads: cheap enough to always track */ - int room=maxctx-sc->len-1; if(r->maximum>room){r->maximum=room; r->length_limited=1;} + /* Clamp to the KV room WITHOUT flagging: length_limited must mean "the + * limit is what stopped us", not "the request asked for more than the + * context could ever hold" — clients that default max_tokens to the + * context size would otherwise see finish_reason=length on every + * naturally-completed generation. The flag is set at the actual + * emitted>=maximum stops below instead. */ + int room=maxctx-sc->len-1; if(r->maximum>room) r->maximum=room; g_temp=r->temp; g_nuc=r->top_p; int next=pick_tok(logit,m->c.vocab,-1); free(logit); - if(r->maximum<=0 || next==eos || is_stop(next)){ mux_done(m,sc,r); return 1; } + if(r->maximum<=0){ r->length_limited=1; mux_done(m,sc,r); return 1; } /* no room at all */ + if(next==eos || is_stop(next)){ mux_done(m,sc,r); return 1; } r->pending=next; r->emitted=1; r->active=1; sc->hist[sc->len]=next; m->n_emit++; if(grd[sub.slot].on){ grammar_reset(&grd[sub.slot]); gr_feed(&grd[sub.slot],next); } mux_data(T,r->id,next); - if(r->emitted>=r->maximum) mux_done(m,sc,r); + if(r->emitted>=r->maximum){ r->length_limited=1; mux_done(m,sc,r); } return 1; } @@ -5696,7 +5703,7 @@ static void run_serve_mux(Model *m, const char *snap){ r->pending=next; sc->hist[sc->len]=next; r->emitted++; m->n_emit++; if(gd->on) gr_feed(gd,next); mux_data(&T,r->id,next); - if(r->emitted>=r->maximum){ mux_done(m,sc,r); done=1; break; } + if(r->emitted>=r->maximum){ r->length_limited=1; mux_done(m,sc,r); done=1; break; } if(jacc++; @@ -5720,7 +5727,7 @@ static void run_serve_mux(Model *m, const char *snap){ r->pending=next; sc->hist[sc->len]=next; r->emitted++; m->n_emit++; if(grd[i].on) gr_feed(&grd[i],next); /* walker stays in sync when not drafting */ mux_data(&T,r->id,next); - if(r->emitted>=r->maximum) mux_done(m,sc,r); + if(r->emitted>=r->maximum){ r->length_limited=1; mux_done(m,sc,r); } } free(lo); } From 94917709a06762504faa5c81f908ef9939a027ec Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Fri, 24 Jul 2026 09:49:39 +0800 Subject: [PATCH 46/96] install: ship version.py to libexec and make the launcher's version import survive its absence (#575) --- c/Makefile | 2 +- c/coli | 13 ++++++++++++- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/c/Makefile b/c/Makefile index c61d6485..6860906b 100644 --- a/c/Makefile +++ b/c/Makefile @@ -512,7 +512,7 @@ install: colibri$(EXE) olmoe$(EXE) $(INSTALL) -m 755 coli $(DESTDIR)$(BINDIR)/coli $(INSTALL) -m 755 colibri$(EXE) $(DESTDIR)$(LIBEXECDIR)/colibri$(EXE) $(INSTALL) -m 755 olmoe$(EXE) $(DESTDIR)$(LIBEXECDIR)/olmoe$(EXE) - $(INSTALL) -m 644 resource_plan.py doctor.py openai_server.py $(DESTDIR)$(LIBEXECDIR)/ + $(INSTALL) -m 644 resource_plan.py doctor.py openai_server.py version.py $(DESTDIR)$(LIBEXECDIR)/ $(INSTALL) -m 644 tools/*.py $(DESTDIR)$(LIBEXECDIR)/tools/ uninstall: diff --git a/c/coli b/c/coli index 5864e689..8f56d068 100755 --- a/c/coli +++ b/c/coli @@ -44,7 +44,18 @@ if sys.platform == "win32": HERE = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, HERE) -from version import __version__ as _version +# version.py sits next to this script in a source checkout, but an installed +# layout puts the launcher in $(PREFIX)/bin while the support modules live in +# $(PREFIX)/libexec/colibri — and a packager may strip it entirely (#575, +# FreeBSD port). The launcher must never crash over a version string. +try: + from version import __version__ as _version +except ModuleNotFoundError: + sys.path.insert(0, os.path.join(os.path.dirname(HERE), "libexec", "colibri")) + try: + from version import __version__ as _version + except ModuleNotFoundError: + _version = "unknown" # Run-in-place (source checkout, "cd c && ./coli ..."): the engine, the # support modules (resource_plan.py, doctor.py, openai_server.py) and From c5f7c91e4283019136b768dd643e89e2778da1de Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Fri, 24 Jul 2026 10:07:46 +0800 Subject: [PATCH 47/96] =?UTF-8?q?web:=20ship=20the=20Expert=20Atlas=20data?= =?UTF-8?q?=20=E2=80=94=20Brain=20hover=20has=20fetched=20/experts.json=20?= =?UTF-8?q?since=20#126,=20but=20the=20file=20never=20shipped?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- web/public/experts.json | 1 + 1 file changed, 1 insertion(+) create mode 100644 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21cbc29b9d353698b029db8d475925111c466e71 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Fri, 24 Jul 2026 10:19:22 +0800 Subject: [PATCH 48/96] =?UTF-8?q?convert:=20default=20to=20group-scaled=20?= =?UTF-8?q?int4=20(gs64)=20=E2=80=94=20close=20the=20#455=20tracking=20ite?= =?UTF-8?q?m=20and=20stop=20steering=20new=20users=20into=20the=20per-row?= =?UTF-8?q?=20trap?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- c/coli | 5 ++++- c/tools/convert_fp8_to_int4.py | 14 +++++++++++--- docs/quickstart.md | 20 ++++++++++++++------ 3 files changed, 29 insertions(+), 10 deletions(-) diff --git a/c/coli b/c/coli index 5864e689..59612eff 100755 --- a/c/coli +++ b/c/coli @@ -936,7 +936,8 @@ def cmd_convert(a): venv_py = os.path.join(HERE, "mio_env", "Scripts" if sys.platform == "win32" else "bin", "python3") py = venv_py if os.path.exists(venv_py) else sys.executable base=[py, os.path.join(TOOLS,"convert_fp8_to_int4.py"), - "--repo", a.repo, "--outdir", a.model, "--ebits", str(a.ebits), "--io-bits", str(a.io_bits)] + "--repo", a.repo, "--outdir", a.model, "--ebits", str(a.ebits), "--io-bits", str(a.io_bits), + "--group-size", str(a.group_size)] if a.xbits: base+=["--xbits",str(a.xbits)] # passo 1: modello principale (78 layer). Resumabile: riparte dagli shard mancanti. print(f" {C.dim}[1/2] model: {' '.join(base)}{C.r}") @@ -1010,6 +1011,8 @@ def main(): pb.add_argument("--data",default=_bench_cache) pc=sub.add_parser("convert", parents=[common]); pc.add_argument("--repo",default="zai-org/GLM-5.2-FP8") pc.add_argument("--ebits",type=int,default=4); pc.add_argument("--io-bits",type=int,default=8); pc.add_argument("--xbits",type=int,default=0) + pc.add_argument("--group-size",type=int,default=64, + help="int4 scale group size: 64 (default, group-scaled quality) or 0 (legacy per-row)") pc.add_argument("--no-mtp",action="store_true",help="skip the MTP head (no speculative drafts)") a=ap.parse_args() handler={"build":cmd_build,"info":cmd_info,"plan":cmd_plan,"doctor":cmd_doctor, diff --git a/c/tools/convert_fp8_to_int4.py b/c/tools/convert_fp8_to_int4.py index 6647e071..998f8288 100644 --- a/c/tools/convert_fp8_to_int4.py +++ b/c/tools/convert_fp8_to_int4.py @@ -353,8 +353,16 @@ def main(): help="bits for other attention projections (q_a, q_b, kv_a). Default=ebits") ap.add_argument("--dmlp-bits", type=int, default=None, help="bits for dense MLP (first 3 layers). Default=ebits") - ap.add_argument("--group-size", type=int, default=0, # 0 = per-row (backward compat); 128 = group-scaled - help="group size for int4 scales: 0=per-row (default), 128=one scale per 128 elements (much better quality)") + ap.add_argument("--group-size", type=int, default=64, + # gs64 is the community-validated default (#225 root cause, #455 5/5-clean + # verification, ablation #453: per-row int4 costs -9.3pp mean acc_norm vs + # -2.2..-3.4pp for group-scaled). Per-row remains available as an explicit + # opt-out; the resume manifest (check_or_record_params) refuses to mix the + # two in one outdir, so a resumed pre-default conversion aborts loudly + # instead of interleaving formats (#355-class). + help="group size for int4 scales: 64=one scale per 64 elements (default, " + "much better quality), 0=per-row (legacy; costs ~9pp on quality " + "benchmarks and is the #455 non-termination trigger)") # Per-projection bit overrides for routed experts (orthogonal to the type-level flags above). ap.add_argument("--up-bits", type=_bits, default=None, help="bits for up_proj in routed experts (e.g. 3 = int3-g64). Default=xbits") @@ -389,7 +397,7 @@ def main(): # EN: this way is repairable in place with tools/repair_mtp_int8.py. print(f"WARNING: --mtp with --ebits {a.ebits} and per-row scales ZEROES eh_proj's " "embedding half -> MTP acceptance ~0% (issue #8). Use the default --ebits 8, " - "or add --group-size 128 for group-scaled int4.") + "or drop --group-size 0 to get the group-scaled default.") if a.xbits is None: a.xbits = a.ebits for proj, val in (("gate_proj", a.gate_bits), ("up_proj", a.up_bits), ("down_proj", a.down_bits)): if val is not None: PROJ_BITS[proj] = val diff --git a/docs/quickstart.md b/docs/quickstart.md index 77f13830..db22a235 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -120,11 +120,18 @@ You have two paths. ### Easiest — download a ready-made int4 container -A pre-converted **GLM-5.2 int4** model is on Hugging Face. **Use the version -with the int8 MTP heads** (the plain int4 heads disable speculative decoding — -see [#8](https://github.com/JustVugg/colibri/issues/8)): +A pre-converted **GLM-5.2 int4** model is on Hugging Face. Use the +**group-scaled (gs64)** container with the **int8 MTP head**: -**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp** +**https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp** + +Group scales matter: the older per-row int4 containers +(`mateogrgic/…-int4-with-int8-mtp`, `jlnsrk/…`) measure ~9pp worse on quality +benchmarks and are the root cause of the think-mode loops and never-terminating +generations in [#455](https://github.com/JustVugg/colibri/issues/455) — the +gs64 container cured every failing case in that report. (The int8 MTP head is +also required: plain int4 heads disable speculative decoding, see +[#8](https://github.com/JustVugg/colibri/issues/8).) Download it into a folder on a fast disk, e.g. `/nvme/glm52_i4` (Linux/macOS) or `D:\glm52_i4` (Windows). It is about **372 GB**, so make sure you have the space. @@ -138,8 +145,9 @@ never needs the full ~756 GB on disk at once: ./coli convert --model /nvme/glm52_i4 ``` -This step uses Python and runs only once. Safe to interrupt and re-run — it -resumes where it left off. +This produces a group-scaled (gs64) container — the same quality-validated +format as the recommended download. This step uses Python and runs only once. +Safe to interrupt and re-run — it resumes where it left off. --- From 7ceb3a024bde764b2438bbbcf90f771616b2323c Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Fri, 24 Jul 2026 11:47:43 +0800 Subject: [PATCH 49/96] =?UTF-8?q?serve:=20single-slot=20MTP=20speculation?= =?UTF-8?q?=20in=20run=5Fserve=5Fmux=20(KV=5FSLOTS=3D1)=20=E2=80=94=20#492?= =?UTF-8?q?/#358?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- c/colibri.c | 81 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 68 insertions(+), 13 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 82eeac21..71c2f1af 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -4697,7 +4697,7 @@ static void intr_install(void){ static void intr_install(void){} #endif static int spec_decode(Model *m, int *all, int kv, int n_new, int eos, float *logit, - void (*emit)(int,void*), void *ud, int *kv_out){ + void (*emit)(int,void*), void *ud, int *kv_out, float **logit_out){ Cfg *c=&m->c; int V=c->vocab; int emitted=0, done=0; int draft[64]; if(g_draft>63) g_draft=63; int carry_ban=-1; /* token rifiutato dalla verifica: escluso dal resample */ @@ -4772,7 +4772,12 @@ static int spec_decode(Model *m, int *all, int kv, int n_new, int eos, float *lo repin_pass(m); /* safe point: all device work is synchronized */ } g_spec_live = 0; /* prefill/decode successivi: gate normali / next prefill: normal gates */ - if(logit) free(logit); + /* logit_out (mux chunking): hand the continuation logits to the caller. + * NULL here means the loop exited right after emitting a token that was + * never forwarded — it sits at all[kv], and the caller must forward it + * before the next chunk. */ + if(logit_out) *logit_out=logit; + else if(logit) free(logit); if(kv_out) *kv_out=kv; return emitted; } @@ -4881,7 +4886,7 @@ static void generate(Model *m, const int *prompt, int np, int n_new, int *out){ for(int i=0;ihits+m->miss; int nsp=0; for(int i=0;in_layers;i++) if(m->L[i].sparse) nsp++; @@ -5418,6 +5423,10 @@ static void serve_ctx_free(Model *m, ServeCtx *s){ typedef struct { int active, pending, emitted, maximum, prompt_tokens, length_limited; + int spec; /* single-slot speculation (#492): decode runs through + spec_decode chunks instead of the shared batch */ + float *spec_logit; /* continuation logits between chunks; NULL = the last + emitted token sits at hist[len], not yet forwarded */ unsigned long long id; float temp, top_p; double started; @@ -5432,6 +5441,10 @@ static void mux_data(Tok *T, unsigned long long id, int token){ fflush(stdout); } +/* emit callback for the single-slot speculative path: stream straight to the mux protocol */ +typedef struct { Tok *T; unsigned long long id; } MuxEmit; +static void mux_spec_emit(int t, void *ud){ MuxEmit *e=(MuxEmit*)ud; mux_data(e->T,e->id,t); } + static void mux_done(Model *m, ServeCtx *sc, ServeReq *r){ double dt=now_s()-r->started; if(dt<1e-6) dt=1e-6; double dh=(double)(m->hits-r->hits0), dm=(double)(m->miss-r->miss0); @@ -5460,6 +5473,8 @@ static void mux_done(Model *m, ServeCtx *sc, ServeReq *r){ * share the batched forwards, so the shares describe the engine, not the * single request (same convention as the STAT hit%% above). */ if(g_prof) prof_report(m,&r->pb,dt,r->emitted,stderr); + if(r->spec_logit){ free(r->spec_logit); r->spec_logit=NULL; } + r->spec=0; r->active=0; } @@ -5478,6 +5493,8 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g } for(int i=0;ipb); /* a few loads: cheap enough to always track */ int room=maxctx-sc->len-1; if(r->maximum>room){r->maximum=room; r->length_limited=1;} g_temp=r->temp; g_nuc=r->top_p; + /* Single-slot speculation (#492/#358): with one KV slot there is no ragged + * batch — the decode is a single contiguous sequence, exactly the regime + * spec_decode already serves in chat/run/run_serve. Hand the request to the + * scheduler's chunked spec path instead of the pending-token cycle; grammar + * requests keep the forced-draft path (its acceptance is ~1 where it fires, + * and the two draft sources would fight over the same forward). */ + if(g_draft>0 && nctx==1 && !grd[sub.slot].on){ + if(r->maximum<=0){ free(logit); mux_done(m,sc,r); return 1; } + r->spec=1; r->spec_logit=logit; r->active=1; + return 1; + } int next=pick_tok(logit,m->c.vocab,-1); free(logit); if(r->maximum<=0 || next==eos || is_stop(next)){ mux_done(m,sc,r); return 1; } r->pending=next; r->emitted=1; r->active=1; sc->hist[sc->len]=next; m->n_emit++; @@ -5608,13 +5636,18 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g static void run_serve_mux(Model *m, const char *snap){ char tkp[2048]; snprintf(tkp,sizeof(tkp),"%s/tokenizer.json",snap); Tok T; tok_load(&T,tkp); int eos=tok_id_of(&T,"<|endoftext|>"); stops_arm_tok(&m->c,eos,&T); - g_draft=0; /* one scheduler owns every forward; MTP/n-gram speculation is not ragged-safe. - * Grammar-forced drafts ARE mux-safe (below): a drafting slot leaves the shared - * batch for one forward and runs the proven single-sequence verify path - * (kv_bind + step_all), exactly like prefill already does per submission. */ int maxctx=getenv("CTX")?atoi(getenv("CTX")):4096; int nctx=getenv("KV_SLOTS")?atoi(getenv("KV_SLOTS")):1; if(nctx<1||nctx>512){fprintf(stderr,"KV_SLOTS must be between 1 and 512\n");exit(2);} + /* MTP/n-gram speculation is not ragged-safe across KV slots, so multi-slot + * serve keeps one scheduler owning every forward (g_draft=0). At KV_SLOTS=1 + * there IS no ragged batch — decode is one contiguous sequence, the exact + * regime spec_decode already serves in chat/run/run_serve — so the engine's + * resolved draft setting stays live (#492/#358). Grammar-forced drafts stay + * mux-safe on every slot count, as before. */ + if(nctx>1) g_draft=0; + else if(g_draft>0) + fprintf(stderr,"[MTP] single-slot serve: speculation active (draft=%d)\n",g_draft); g_kvsave=getenv("KVSAVE")?atoi(getenv("KVSAVE")):1; KVState *initial=m->kv; free(initial->kv_start); free(initial); ServeCtx *ctx=calloc(nctx,sizeof(*ctx)); ServeReq *req=calloc(nctx,sizeof(*req)); @@ -5674,6 +5707,24 @@ static void run_serve_mux(Model *m, const char *snap){ DecodeRow rows[512]; int slots[512], S=0; for(int i=0;ispec){ + /* Single-slot speculative decode (#492): KV_SLOTS=1 has no ragged + * batch, so the whole turn runs through spec_decode in one call — + * the exact contract run_serve (non-mux) already uses. No chunk + * splicing (that would re-enter spec_decode mid-turn and double + * the boundary token); Ctrl-C interrupts through g_intr inside + * spec_decode, same as every other serve path. r->spec_logit + * holds the prefill continuation from mux_submit. */ + kv_bind(m,&sc->kv); + g_temp=r->temp; g_nuc=r->top_p; + float *lg=r->spec_logit; r->spec_logit=NULL; /* spec_decode takes ownership */ + MuxEmit ud={&T,r->id}; + int prod=spec_decode(m,sc->hist,sc->len,r->maximum-r->emitted,eos,lg, + mux_spec_emit,&ud,&sc->len,NULL); + r->emitted+=prod; + mux_done(m,sc,r); + continue; /* whole turn handled outside the shared batch */ + } /* grammar-forced drafts (greedy requests only: verification under sampling * needs rejection resampling, out of scope here). The slot leaves the shared * batch for one forward and runs the single-sequence verify path. */ @@ -5787,7 +5838,7 @@ static void run_serve(Model *m, const char *snap){ float *logit=step(m,hist+len-1,1,len-1); EmitStream es={&T,m,now_s(),0,1}; int prod=0; - if(cur>0) prod=spec_decode(m,hist,len,cur,eos,logit,emit_stream,&es,&len); + if(cur>0) prod=spec_decode(m,hist,len,cur,eos,logit,emit_stream,&es,&len,NULL); else free(logit); double tdt=now_s()-tt0; if(tdt<1e-6) tdt=1e-6; double dh=(double)(m->hits-h0), dm=(double)(m->miss-ms0); @@ -5870,7 +5921,7 @@ static void run_serve(Model *m, const char *snap){ EmitStream es={&T,m,now_s(),0,1}; int prod=0; grammar_reset(&g_grd); /* nuova risposta = nuovo documento (MORE invece continua) */ - if(cur>0) prod=spec_decode(m,hist,len,cur,eos,logit,emit_stream,&es,&len); + if(cur>0) prod=spec_decode(m,hist,len,cur,eos,logit,emit_stream,&es,&len,NULL); else free(logit); double tdt=now_s()-tt0; if(tdt<1e-6) tdt=1e-6; double dh=(double)(m->hits-h0), dm=(double)(m->miss-ms0); @@ -6786,15 +6837,19 @@ int main(int argc, char **argv){ * altrimenti "MTP active (draft=8)" mentirebbe: il messaggio e' stampato * prima della scelta del path (run_serve_mux, sotto), e con DRAFT=8 diceva * "active" per poi disabilitarlo in silenzio (#358, LordMZTE). */ - int mux_will_disable_mtp = getenv("SERVE") && getenv("SERVE_BATCH") && atoi(getenv("SERVE_BATCH")); + /* Multi-slot mux only: at KV_SLOTS=1 there is no ragged batch and the mux + * keeps the resolved draft setting live (#492 single-slot speculation). */ + int mux_slots = getenv("KV_SLOTS") ? atoi(getenv("KV_SLOTS")) : 1; + int mux_will_disable_mtp = getenv("SERVE") && getenv("SERVE_BATCH") && + atoi(getenv("SERVE_BATCH")) && mux_slots>1; int eff_draft = mux_will_disable_mtp ? 0 : g_draft; printf("loaded in %.2fs | resident dense: %.2f MB | layers=%d experts=%d | MTP %s (draft=%d)\n", now_s()-t0, m.resident_bytes/(1024.0*1024.0), m.c.n_layers, m.c.n_experts, m.has_mtp?(mux_will_disable_mtp?"DISABLED (multiplexed serve)":"ACTIVE"):"absent", eff_draft); /* anche su stderr: e' il canale che le UI (coli) mostrano all'utente */ if(mux_will_disable_mtp && m.has_mtp) - fprintf(stderr,"[MTP] disabled in multiplexed serve (SERVE_BATCH=1): speculation is not " - "ragged-safe across KV slots. Single-client interactive use (`coli chat`) keeps MTP.\n"); + fprintf(stderr,"[MTP] disabled in multiplexed serve (SERVE_BATCH=1, KV_SLOTS>1): speculation is " + "not ragged-safe across KV slots. Single-slot serve (KV_SLOTS=1) keeps MTP.\n"); else fprintf(stderr,"[MTP] %s (draft=%d)\n", m.has_mtp?"active: native speculative decoding":"absent", eff_draft); #ifdef __linux__ From aa65e21df29b64f398b2cb1b9c0cdcd6ae3ec245 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Fri, 24 Jul 2026 12:09:34 +0800 Subject: [PATCH 50/96] =?UTF-8?q?docker:=20add=20slim=20multi-stage=20imag?= =?UTF-8?q?e=20(Dockerfile.slim)=20+=20compose=20=E2=80=94=20115MB=20non-r?= =?UTF-8?q?oot=20portable=20build=20alongside=20the=20beginner=20Dockerfil?= =?UTF-8?q?e?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .dockerignore | 20 +++++++++++++++ docker/Dockerfile.slim | 54 +++++++++++++++++++++++++++++++++++++++ docker/README.md | 44 +++++++++++++++++++++++++++++++ docker/docker-compose.yml | 33 ++++++++++++++++++++++++ 4 files changed, 151 insertions(+) create mode 100644 .dockerignore create mode 100644 docker/Dockerfile.slim create mode 100644 docker/docker-compose.yml diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..2ad637b4 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,20 @@ +# The Docker build only needs c/ (engine sources + launcher). Everything else +# is either huge, host-specific, or irrelevant to the image. +.git +web/node_modules +web/dist +site +docs +**/*.o +**/*.exe +c/colibri +c/glm +c/olmoe +c/glm_tiny +**/.coli_usage +**/.coli_kv* +**/__pycache__ +*.safetensors +*.log +CODEX-BRIEF.md +wt-inc4 diff --git a/docker/Dockerfile.slim b/docker/Dockerfile.slim new file mode 100644 index 00000000..e41bab5a --- /dev/null +++ b/docker/Dockerfile.slim @@ -0,0 +1,54 @@ +# colibri — CPU inference image (multi-stage: build then a slim runtime) +# +# The engine is portable C with a Python launcher/gateway. This image ships +# ONLY the inference path (chat/serve/run/info/plan/doctor) — the ~372 GB +# model is mounted, never baked in, and the converter (which needs torch) is +# deliberately out of scope; convert from a source checkout instead. +# +# docker build -t colibri -f docker/Dockerfile.slim . +# docker run --rm -v /nvme/glm52_i4:/model colibri info +# docker run --rm -it -v /nvme/glm52_i4:/model colibri chat --ram 24 +# docker run --rm -p 5000:5000 -v /nvme/glm52_i4:/model colibri \ +# serve --host 0.0.0.0 --model-id glm-5.2 +# +# -------------------------------------------------------------------------- +# Stage 1: build the engine. ARCH is x86-64-v3 (portable AVX2) via `make +# portable`'s target, NOT native — a native build bakes in the builder CPU's +# ISA and SIGILLs on a different host. Override ARCH at build time to pin one. +FROM debian:stable-slim AS build +ARG ARCH=x86-64-v3 +RUN apt-get update && \ + apt-get install -y --no-install-recommends build-essential make && \ + rm -rf /var/lib/apt/lists/* +WORKDIR /src +COPY c/ ./c/ +RUN make -C c colibri ARCH=${ARCH} + +# -------------------------------------------------------------------------- +# Stage 2: runtime. openai_server.py and the launcher use only the Python +# standard library, so no pip install — just python3 + the built binary and +# its support files. libgomp1 provides the OpenMP runtime the engine links. +FROM debian:stable-slim +RUN apt-get update && \ + apt-get install -y --no-install-recommends python3 libgomp1 && \ + rm -rf /var/lib/apt/lists/* && \ + useradd -m -u 1000 colibri +WORKDIR /app + +# The engine binary plus the launcher and the files it resolves at runtime +# (version.py, the gateway, resource planner, doctor, tokenizer headers, tools). +COPY --from=build /src/c/colibri ./colibri +COPY c/coli c/version.py c/openai_server.py c/resource_plan.py c/doctor.py ./ +COPY c/tools/ ./tools/ + +ENV COLI_MODEL=/model \ + COLI_ENGINE=/app/colibri \ + PYTHONUNBUFFERED=1 +# A bind-mounted model lands here; declaring the volume documents the contract. +VOLUME ["/model"] +# `coli serve` default port. +EXPOSE 5000 + +USER colibri +ENTRYPOINT ["python3", "/app/coli"] +CMD ["info"] diff --git a/docker/README.md b/docker/README.md index 9f5800c0..aed1a574 100644 --- a/docker/README.md +++ b/docker/README.md @@ -485,4 +485,48 @@ PS C:\quack\llm\colibri\docker> docker run --rm -it --name colibri-c -v "C:\quac ``` +--- + +## Slim image (developers / production) + +The guide above uses `Dockerfile`, which clones the repo inside the image — one +file to download, nothing else on your machine. For CI, servers, or anyone who +already has the repo checked out, `Dockerfile.slim` is a multi-stage build that +produces a **~115 MB** image (vs. the full toolchain the clone-in-image keeps +around), runs as a **non-root** user, and compiles the engine with a +**portable** ISA (`x86-64-v3`) so the image runs on any modern x86-64 host +instead of only the machine that built it. + +Build it from the repo root (it needs the `c/` sources as context): + +```bash +git clone https://github.com/JustVugg/colibri.git && cd colibri +docker build -t colibri -f docker/Dockerfile.slim . +``` + +Run — the model is bind-mounted at `/model`, never baked in: + +```bash +docker run --rm -v /nvme/glm52_i4:/model colibri info +docker run --rm -it -v /nvme/glm52_i4:/model colibri chat --ram 24 +docker run --rm -p 5000:5000 -v /nvme/glm52_i4:/model colibri \ + serve --host 0.0.0.0 --model-id glm-5.2 --ram 24 +``` + +The entrypoint is `coli`, so the first argument is the subcommand (`info`, +`chat`, `serve`, `run`, `plan`, `doctor`) — no `./coli` prefix. The container +runs as UID 1000, so the model directory must be readable by that user +(a directory you own already is; a root-owned one needs `chmod -R a+rX` or a +matching `--user`). + +### One-command server (docker compose) + +```bash +MODEL_DIR=/nvme/glm52_i4 COLI_RAM=24 docker compose -f docker/docker-compose.yml up +``` + +Serves the OpenAI-compatible API on `localhost:5000`. The converter (which +needs PyTorch) is intentionally not in this image — convert from a source +checkout, then point `MODEL_DIR` at the result. + [source: 1] \ No newline at end of file diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 00000000..bcf0e30a --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,33 @@ +# colibri — OpenAI-compatible server in one command. +# +# MODEL_DIR=/nvme/glm52_i4 docker compose -f docker/docker-compose.yml up +# +# Then: curl http://localhost:5000/v1/chat/completions -d '{"model":"glm-5.2", +# "messages":[{"role":"user","content":"Hi"}]}' +# +# The model is bind-mounted read-only from MODEL_DIR on the host — keep it on +# NVMe/ext4, never a network mount (expert streaming is latency-bound). +services: + colibri: + build: + context: .. + dockerfile: docker/Dockerfile.slim + image: colibri + ports: + - "5000:5000" + volumes: + - "${MODEL_DIR:?set MODEL_DIR to the int4 model directory}:/model:ro" + environment: + # RAM budget in GB: more cached experts = faster. Size to the host. + COLI_RAM: "${COLI_RAM:-24}" + command: + - serve + - --host + - 0.0.0.0 + - --model-id + - glm-5.2 + - --ram + - "${COLI_RAM:-24}" + restart: unless-stopped + # Streaming a 744B model touches a lot of page cache; give it headroom. + shm_size: "2gb" From 926dde9af3b2f19faa1cb7d1e608de0ee8f44417 Mon Sep 17 00:00:00 2001 From: bill Date: Fri, 24 Jul 2026 09:56:43 -0400 Subject: [PATCH 51/96] fix(windows): enable direct I/O for mirror shards --- c/st.h | 2 +- c/tests/test_st_mirror.c | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/c/st.h b/c/st.h index ab259ae2..8b911d38 100644 --- a/c/st.h +++ b/c/st.h @@ -169,7 +169,7 @@ static int st_mirror_init(shards *S, const char *dir) { S->mfds[i] = mfd; #ifdef O_DIRECT S->mdfds[i] = open(mp, COMPAT_O_RDONLY | O_DIRECT); -#elif defined(__APPLE__) +#elif defined(__APPLE__) || defined(_WIN32) S->mdfds[i] = compat_open_direct(mp); #endif S->nmirror++; diff --git a/c/tests/test_st_mirror.c b/c/tests/test_st_mirror.c index 769c99d6..e520d64b 100644 --- a/c/tests/test_st_mirror.c +++ b/c/tests/test_st_mirror.c @@ -83,6 +83,9 @@ int main(void) { CHECK(st_mirror_init(&S, DIR_B) == 1); int mfd = st_fd_rep(&S, t->fd, 1); CHECK(mfd >= 0 && mfd != t->fd); +#ifdef _WIN32 + CHECK(st_direct_fd_rep(&S, t->fd, 1) >= 0); +#endif float a[8], b[8]; CHECK(pread(t->fd, a, t->nbytes, t->off) == t->nbytes); CHECK(pread(mfd, b, t->nbytes, t->off) == t->nbytes); From 00561ef4e96e7e80c1c589f7210569ef98d9e9ec Mon Sep 17 00:00:00 2001 From: Grecco Date: Fri, 24 Jul 2026 16:05:29 -0300 Subject: [PATCH 52/96] feat: enable rocWMMA tensor cores for AMD HIP backend Activate W4A16 WMMA kernels in backend_gpu_compat.h (Phase 2): - #include (with __has_include guard) - COLI_GPU_HAS_WMMA=1 (was 0) - #define __CUDA_ARCH__ 700 (activates >= 700 guard for w4a16 kernels) - #define __half rocwmma::float16_t - namespace nvcuda { wmma = ::rocwmma; } - #define __syncwarp() __syncthreads() (ROCm 6.2 compatibility) Other changes: - Makefile: add -fPIE to HIPCCFLAGS (PIE-default GCC link fix) - coli: cuda_binary() detects libamdhip64 in addition to libcudart - ci.yml: install rocwmma-dev in HIP syntax check job - .gitignore: add ai_plans/ (local planning directory) Tested on RX 7900 XTX (gfx1100, ROCm 6.16.13): - make -C c check: all C + Python tests pass (0 failures) - make gpu-compile HIP=1: compiles clean (0 errors) - coli serve --port 8080: OpenAI API works with streaming - GLM-5.2 744B int4: 0.33 tok/s (was 0.22, +50%) - Oracle: 32/32 TF, 20/20 greedy match --- .github/workflows/ci.yml | 2 +- .gitignore | 4 ++++ c/Makefile | 2 +- c/backend_gpu_compat.h | 27 +++++++++++++++++++++------ c/coli | 3 ++- 5 files changed, 29 insertions(+), 9 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 426ea413..84278c10 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -58,7 +58,7 @@ jobs: # link of the test binary below covers the single-source claim, and # the engine link is exercised on real ROCm hardware (GPU_BACKENDS.md). docker run --rm -v "$PWD":/src -w /src rocm/dev-ubuntu-24.04:6.2 bash -c ' - apt-get update && apt-get install -y --no-install-recommends make gcc g++ && + apt-get update && apt-get install -y --no-install-recommends make gcc g++ rocwmma-dev && make -C c gpu-compile HIP=1 HIP_ARCH=gfx1100' echo "HIP syntax check passed" diff --git a/.gitignore b/.gitignore index f62b3792..05cc9b89 100644 --- a/.gitignore +++ b/.gitignore @@ -68,3 +68,7 @@ olmoe_merged/ olmoe_i4/ c/olmoe_merged/ c/olmoe_i4/ + +# planning docs — internal, not part of the project +ai_plans/ +PR_DESCRIPTION.md diff --git a/c/Makefile b/c/Makefile index a598d923..48bdca34 100644 --- a/c/Makefile +++ b/c/Makefile @@ -183,7 +183,7 @@ HIP ?= 0 ROCM_HOME ?= /opt/rocm HIPCC ?= $(ROCM_HOME)/bin/hipcc HIP_ARCH ?= native -HIPCCFLAGS ?= -O3 -std=c++17 -x hip --offload-arch=$(HIP_ARCH) -Wall -Wextra +HIPCCFLAGS ?= -O3 -std=c++17 -x hip --offload-arch=$(HIP_ARCH) -Wall -Wextra -fPIE GPUCC = $(NVCC) GPUFLAGS = $(NVCCFLAGS) GPUCC_NAME = nvcc (set CUDA_HOME or NVCC) diff --git a/c/backend_gpu_compat.h b/c/backend_gpu_compat.h index 990462af..91b9f285 100644 --- a/c/backend_gpu_compat.h +++ b/c/backend_gpu_compat.h @@ -8,17 +8,32 @@ * * COLI_GPU_HAS_WMMA: the WMMA tensor-core kernels are guarded by * __CUDA_ARCH__ >= 700 (device side) and by this flag at the host dispatch - * sites. Under HIP the flag is 0: gfx GPUs report compute_major >= 7, so a - * runtime-only check would select empty kernel bodies. Matrix-core support - * via rocWMMA is a possible follow-up; until then HIP always uses the - * portable kernels. */ + * sites. Under HIP, __CUDA_ARCH__ is not defined by hipcc, so we define it + * here to 700 to activate the WMMA kernel bodies (the guard is a minimum + * threshold; 700 satisfies >= 700). Note: the grouped_s4_wmma kernel has a + * stricter guard of __CUDA_ARCH__ >= 750, so it stays a no-op even with + * __CUDA_ARCH__=700 — see HIP-KNOWN-BUGS.md BUG-002. + * rocWMMA provides float16_t and the fragment/mma_sync API; the CUDA + * __half and nvcuda::wmma names are mapped to their rocWMMA equivalents. + * Note: grouped_s4_wmma uses wmma::experimental::precision::s4 (4-bit) + * which has no rocWMMA equivalent — that kernel stays a no-op under HIP + * and falls back to quant_matmul. */ #ifndef COLIBRI_BACKEND_GPU_COMPAT_H #define COLIBRI_BACKEND_GPU_COMPAT_H #if defined(__HIP_PLATFORM_AMD__) || defined(__HIP__) #include #include -#define COLI_GPU_HAS_WMMA 0 +#if __has_include() +#include +#define COLI_GPU_HAS_WMMA 1 +#define __CUDA_ARCH__ 700 +#define __half rocwmma::float16_t +namespace nvcuda { namespace wmma = ::rocwmma; } +#define __syncwarp() __syncthreads() +#else +#error "rocWMMA headers not found. Install rocwmma-dev (or rocm-hip-runtime-dev) to build with HIP." +#endif #define cudaError_t hipError_t #define cudaSuccess hipSuccess #define cudaGetErrorString hipGetErrorString @@ -63,4 +78,4 @@ #define COLI_GPU_HAS_WMMA 1 #endif -#endif +#endif /* COLIBRI_BACKEND_GPU_COMPAT_H */ diff --git a/c/coli b/c/coli index 5864e689..ad0343a7 100755 --- a/c/coli +++ b/c/coli @@ -154,7 +154,8 @@ def cuda_binary(): if sys.platform == "linux": try: linked=subprocess.run(["ldd",GLM],capture_output=True,text=True,timeout=3) - return any("libcudart" in line and "not found" not in line + # Detect both CUDA (libcudart) and HIP (libamdhip64) builds. + return any(("libcudart" in line or "libamdhip64" in line) and "not found" not in line for line in linked.stdout.splitlines()) except (OSError,subprocess.SubprocessError): return False if sys.platform == "win32": From 0d5df23ed0f8fb203781127b1c924af5438de75e Mon Sep 17 00:00:00 2001 From: Grecco Date: Fri, 24 Jul 2026 17:18:44 -0300 Subject: [PATCH 53/96] Removing local folder from ignore file. --- .gitignore | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.gitignore b/.gitignore index 3e63817c..7ac3789d 100644 --- a/.gitignore +++ b/.gitignore @@ -70,7 +70,3 @@ olmoe_i4/ c/olmoe_merged/ c/olmoe_i4/ c/tiny_inkling/ - -# planning docs — internal, not part of the project -ai_plans/ -PR_DESCRIPTION.md From ce80dc02173e2d349f8a3227471e049ff7cc81ab Mon Sep 17 00:00:00 2001 From: lBroth Date: Fri, 24 Jul 2026 22:36:49 +0200 Subject: [PATCH 54/96] metal: compile-time SDK floor for the residency-set path METAL=1 failed to build against a macOS SDK older than 15. The COLI_METAL_RESSET block is guarded by @available(macOS 15.0, *), but that is a runtime check: the compiler still resolves MTLResidencySet, MTLResidencySetDescriptor and the newResidencySetWithDescriptor: / addResidencySet: / removeResidencySet: selectors against the SDK headers, which do not declare them before macOS 15. Add COLI_HAS_RESSET (__MAC_OS_X_VERSION_MAX_ALLOWED >= 150000, overridable with -DCOLI_HAS_RESSET=0) and gate the five sites that touch the API: the three resset_* helpers (same signatures, no-ops below the floor), the creation block in the init path (with an #else that reports the gate is compiled out, instead of silently doing nothing), and the removeResidencySet: call in coli_metal_shutdown. The @available guards are left in place, so a macOS 15 SDK build still behaves correctly at runtime on macOS 14. With the gate on, the emitted object is byte identical to stock, so builds on a macOS 15+ SDK are unaffected. Verified against a reconstructed SDK 14 header set (Metal.framework with MTLResidencySet.h removed and the residency-set declarations stripped): stock fails with the 11 errors from the report, this compiles clean. Repo checks on macOS 26.5 / M5 Pro 48 GB: make check 142 tests, METAL=1 build with no warnings, metal-test green, oracle 32/32 TF and 20/20 greedy. docs/metal.md gains a note about the macOS 15 SDK floor for the path. --- c/backend_metal.mm | 31 ++++++++++++++++++++++++++++++- docs/metal.md | 2 ++ 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/c/backend_metal.mm b/c/backend_metal.mm index 2894d791..b894eb06 100644 --- a/c/backend_metal.mm +++ b/c/backend_metal.mm @@ -333,8 +333,20 @@ kernel void r_top8_par(device const float* sig [[buffer(0)]], device const float // g_resset_obj is a bare `id` (holds id) so the global's declared type // carries no availability annotation -- the protocol name only appears inside // @available(macOS 15.0, *) guards below, keeping -Wunguarded-availability clean. +// @available is a runtime guard only: the compiler still resolves MTLResidencySet and its +// selectors against the SDK headers, which don't declare them before macOS 15 (#596). +// COLI_HAS_RESSET is the compile-time floor (helpers become no-ops below it); the @available +// guards stay on top so a macOS 15 SDK build still runs right on macOS 14. -DCOLI_HAS_RESSET=0 +// forces the fallback branch on a modern SDK. +#ifndef COLI_HAS_RESSET +#if defined(__MAC_OS_X_VERSION_MAX_ALLOWED) && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000 +#define COLI_HAS_RESSET 1 +#else +#define COLI_HAS_RESSET 0 +#endif +#endif static id g_resset_obj; -static bool g_resset_enabled; // COLI_METAL_RESSET=1, macOS 15+, and creation succeeded +static bool g_resset_enabled; // COLI_METAL_RESSET=1, macOS 15 SDK+OS, and creation succeeded static bool g_resset_dirty; // addAllocation: calls pending commit; g_resset_mtx-guarded // Set mutations + dirty flag get their OWN mutex, never held together with g_slab_mtx: no // live Metal call may run under the slab lock the parallel OMP loader threads contend on @@ -355,8 +367,12 @@ kernel void r_top8_par(device const float* sig [[buffer(0)]], device const float // no separate counter for the add/remove side. static void resset_add(id b) { if (!g_resset_enabled) return; +#if COLI_HAS_RESSET std::lock_guard lk(g_resset_mtx); if (@available(macOS 15.0, *)) { [(id)g_resset_obj addAllocation:b]; g_resset_dirty = true; } +#else + (void)b; +#endif } // Remove + commit immediately, NOT deferred: the caller frees the underlying host memory // right after coli_metal_unregister returns, so the removal must be applied before that -- @@ -364,22 +380,28 @@ static void resset_add(id b) { // risk the GPU could act on. Also runs outside g_slab_mtx (see g_resset_mtx above). static void resset_remove(id b) { if (!g_resset_enabled) return; +#if COLI_HAS_RESSET std::lock_guard lk(g_resset_mtx); if (@available(macOS 15.0, *)) { id rs = (id)g_resset_obj; [rs removeAllocation:b]; [rs commit]; } g_resset_dirty = false; // commit above also flushes any pending adds +#else + (void)b; +#endif } // Flush pending adds before moe_submit relies on the set alone for residency -- the only // caller that skips per-buffer useResource: (see moe_submit below). Takes g_resset_mtx // only, never g_slab_mtx; the happens-before argument lives at resset_add above. static void resset_flush() { if (!g_resset_enabled) return; +#if COLI_HAS_RESSET std::lock_guard lk(g_resset_mtx); if (!g_resset_dirty) return; if (@available(macOS 15.0, *)) { [(id)g_resset_obj commit]; } g_resset_dirty = false; +#endif } // Harness visibility for the flush cost, which sits OUTSIDE the moe_times setup/gpu // breakdown (timed around resset_flush in moe_submit, before ts_start). Returns whether @@ -455,6 +477,7 @@ static size_t fmt_bytes(int fmt, int I, int O) { fprintf(stderr, "[metal] pipeline failed\n"); g_dev = nil; return 0; } // E5 experiment: COLI_METAL_RESSET=1 -- see g_resset_obj comment above. if (getenv("COLI_METAL_RESSET") && atoi(getenv("COLI_METAL_RESSET"))) { +#if COLI_HAS_RESSET if (@available(macOS 15.0, *)) { MTLResidencySetDescriptor *rd = [MTLResidencySetDescriptor new]; rd.initialCapacity = 4096; // hint only (internal array presize), not a hard limit @@ -471,6 +494,10 @@ static size_t fmt_bytes(int fmt, int I, int O) { } else { fprintf(stderr, "[METAL] COLI_METAL_RESSET=1 requested but OS < macOS 15 -- stock per-CB residency path\n"); } +#else + fprintf(stderr, "[METAL] COLI_METAL_RESSET=1 requested but this binary was built against a " + "macOS SDK < 15 (MTLResidencySet unavailable) -- stock per-CB residency path\n"); +#endif } } return 1; @@ -543,9 +570,11 @@ static size_t fmt_bytes(int fmt, int I, int O) { extern "C" void coli_metal_shutdown(void) { coli_metal_spin_stop(); +#if COLI_HAS_RESSET if (g_resset_enabled) { if (@available(macOS 15.0, *)) { [g_queue removeResidencySet:(id)g_resset_obj]; } } +#endif g_resset_obj=nil; g_resset_enabled=false; g_resset_dirty=false; g_gemv=nil; g_queue=nil; g_dev=nil; g_tensor_count=g_tensor_bytes=0; } diff --git a/docs/metal.md b/docs/metal.md index 5d983003..9d1208d2 100644 --- a/docs/metal.md +++ b/docs/metal.md @@ -10,6 +10,8 @@ Token-exact vs the CPU path. ```bash cd c make colibri METAL=1 # macOS only; no Xcode needed (shader compiles at runtime) + # any macOS SDK builds; the COLI_METAL_RESSET residency-set + # path needs the macOS 15 SDK and is compiled out below it make metal-test # standalone kernel/attention correctness vs CPU reference COLI_METAL=1 COLI_MODEL=/path/glm52_i4 ./coli chat --ram 96 ``` From 594c87e9c183a51078ae72ad913a24cbb98301f9 Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sat, 25 Jul 2026 12:21:07 +0200 Subject: [PATCH 55/96] serve: reverse-proxy Host allowlist + quiet prefill keepalive (#597) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two self-contained gaps from mkelcb's serving report (#597), both in the Python gateway, both behind explicit opt-ins: - #597 item 2 — the DNS-rebinding Host guard accepted only loopback names and the bind address, so a legitimate reverse proxy / Tailscale MagicDNS host in front of the loopback bind was rejected with 403 and had no supported way to be trusted. Add `--allowed-host` (repeatable) and `COLI_ALLOWED_HOSTS` (comma-separated) that extend the allowlist. Default is unchanged (loopback + bind address only); no wildcard, each trusted Host is explicit, and this is orthogonal to CORS and API-key auth. - #597 item 5 — the cold-prefill keepalive streamed a visible `reasoning_content: "."` every 10s, painting hundreds of dots in the client's reasoning panel over a minutes-long prefill. Default to an empty delta (still a valid SSE data event, still resets the client idle timer); COLI_VISIBLE_KEEPALIVE=1 restores the old visible dot for diagnosing keepalive delivery. Items 1 and 4 (non-streaming) of the same report are already fixed on dev by #535 and #568; items 3, 4-streaming and 6 are tracked separately. Tests: 4 new AllowedHostsTest cases (normalisation, trusted host accepted with port/case, untrusted rejected by default and with an allowlist). Full server suite 68/68. Co-Authored-By: Claude Opus 4.8 (1M context) --- c/openai_server.py | 29 +++++++++++++++++---- c/tests/test_openai_server.py | 48 +++++++++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 5 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 0080bd73..91373088 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -1304,7 +1304,7 @@ class APIServer(ThreadingHTTPServer): def __init__(self, address, engine, model_id, api_key=None, max_tokens=1024, cors_origins=DEFAULT_CORS_ORIGINS, max_queue=8, queue_timeout=300, - kv_slots=1): + kv_slots=1, allowed_hosts=()): super().__init__(address, APIHandler) self.engine = engine self.model_id = model_id @@ -1313,6 +1313,11 @@ def __init__(self, address, engine, model_id, api_key=None, max_tokens=1024, self.scheduler = GenerationScheduler(max_queue, queue_timeout, kv_slots) self.kv_slots = kv_slots self.cors_origins = tuple(cors_origins) + # Extra Host header values trusted past the DNS-rebinding guard, for a + # reverse proxy / MagicDNS in front of the loopback bind (#597). Explicit + # opt-in only: no wildcard, default stays loopback + bind address. + self.allowed_hosts = tuple( + h.strip().lower() for h in allowed_hosts if h and h.strip()) self.created = int(time.time()) @@ -1383,6 +1388,7 @@ def _check_host(self): name = host # bare host / bracketless ipv6 name = name.strip().lower() allowed = set(self.LOOPBACK_HOSTS) + allowed.update(self.server.allowed_hosts) # #597: operator-trusted reverse-proxy names try: allowed.add(str(self.server.server_address[0]).strip("[]").lower()) except Exception: @@ -1657,7 +1663,14 @@ def event(choices, usage_marker=False): connected = False def _keepalive(): - ping = [{"index": 0, "delta": ({"reasoning_content": "."} if chat else {"content": ""}), + # #597: an empty delta already resets the client's idle timer without + # painting hundreds of dots in the reasoning panel during a minutes-long + # cold prefill. COLI_VISIBLE_KEEPALIVE=1 restores the old visible "." for + # diagnosing whether keepalives are being delivered at all. + visible = os.environ.get("COLI_VISIBLE_KEEPALIVE") == "1" + ping = [{"index": 0, + "delta": ({"reasoning_content": "." if visible else ""} if chat + else {"content": ""}), "logprobs": None, "finish_reason": None}] while not ka_stop.wait(1.0): if not connected: @@ -2028,7 +2041,7 @@ def completion(self, body, request_id): def serve(model, host="127.0.0.1", port=8000, model_id="glm-5.2-colibri", api_key=None, cap=8, max_tokens=1024, engine=None, env=None, cors_origins=None, - max_queue=8, queue_timeout=300, kv_slots=1): + max_queue=8, queue_timeout=300, kv_slots=1, allowed_hosts=()): if engine is None: engine = default_engine() if not 1 <= max_tokens: @@ -2055,7 +2068,7 @@ def serve(model, host="127.0.0.1", port=8000, model_id="glm-5.2-colibri", api_ke # Bind before starting the 744B engine. A stale/occupied port must fail in # milliseconds rather than loading hundreds of GB and leaking a child. server = APIServer((host, port), None, model_id, api_key, max_tokens, origins, - max_queue, queue_timeout, kv_slots) + max_queue, queue_timeout, kv_slots, allowed_hosts=allowed_hosts) runtime = None previous_sigterm = signal.getsignal(signal.SIGTERM) try: @@ -2090,6 +2103,11 @@ def main(): parser.add_argument("--queue-timeout", type=float, default=float(os.environ.get("COLI_QUEUE_TIMEOUT", "300"))) parser.add_argument("--kv-slots", type=int, default=int(os.environ.get("COLI_KV_SLOTS", "1"))) + parser.add_argument("--allowed-host", action="append", + default=[h.strip() for h in os.environ.get("COLI_ALLOWED_HOSTS", "").split(",") if h.strip()], + help="additional Host header value accepted by the DNS-rebinding guard " + "(reverse proxy / MagicDNS in front of the loopback bind); repeat as needed, " + "or set COLI_ALLOWED_HOSTS as a comma-separated list") args = parser.parse_args() global ARCH ARCH = args.arch @@ -2103,7 +2121,8 @@ def main(): args.model_id = "inkling-colibri" if ARCH == "inkling" else "glm-5.2-colibri" serve(args.model, args.host, args.port, args.model_id, args.api_key, args.cap,args.max_tokens,args.engine,cors_origins=args.cors_origin, - max_queue=args.max_queue,queue_timeout=args.queue_timeout,kv_slots=args.kv_slots) + max_queue=args.max_queue,queue_timeout=args.queue_timeout,kv_slots=args.kv_slots, + allowed_hosts=args.allowed_host) if __name__ == "__main__": diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index a9a31e69..3bba88d0 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -1,3 +1,4 @@ +import http.client import io import json import math @@ -957,5 +958,52 @@ def test_rejects_tool_choice_without_tools(self): generation_options({"messages": [], "tool_choice": "required"}, 128) +class AllowedHostsTest(unittest.TestCase): + """#597: the DNS-rebinding guard must accept operator-trusted reverse-proxy + Host values, while still rejecting everything else by default.""" + + def _make_server(self, allowed_hosts=()): + server = APIServer(("127.0.0.1", 0), FakeEngine(), "test-model", + allowed_hosts=allowed_hosts) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + self.addCleanup(thread.join, 2) + self.addCleanup(server.server_close) + self.addCleanup(server.shutdown) + self.addCleanup(server.scheduler.close) + return server + + def _get_models(self, port, host_header): + # http.client lets us set an arbitrary Host header (urlopen forces its own). + conn = http.client.HTTPConnection("127.0.0.1", port, timeout=2) + try: + conn.putrequest("GET", "/v1/models", skip_host=True) + conn.putheader("Host", host_header) + conn.endheaders() + return conn.getresponse().status + finally: + conn.close() + + def test_allowlist_wiring_normalises_and_filters(self): + server = self._make_server(allowed_hosts=(" Proxy.Example.TS.net ", "", " ")) + self.assertEqual(server.allowed_hosts, ("proxy.example.ts.net",)) + + def test_trusted_reverse_proxy_host_is_accepted(self): + server = self._make_server(allowed_hosts=("proxy.example.ts.net",)) + port = server.server_port + # trusted name (with a port suffix, case-insensitive) passes the guard + self.assertEqual(self._get_models(port, "Proxy.Example.TS.net:8000"), 200) + # loopback still works, unaffected by the allowlist + self.assertEqual(self._get_models(port, "localhost"), 200) + + def test_untrusted_host_is_rejected_by_default(self): + server = self._make_server() # no allowlist: loopback/bind only + self.assertEqual(self._get_models(server.server_port, "evil.example.com"), 403) + + def test_untrusted_host_still_rejected_with_allowlist(self): + server = self._make_server(allowed_hosts=("proxy.example.ts.net",)) + self.assertEqual(self._get_models(server.server_port, "evil.example.com"), 403) + + if __name__ == "__main__": unittest.main() From b2b50e96a4a37a1f762a00cc574bc8b78bb1d0a0 Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sat, 25 Jul 2026 12:44:24 +0200 Subject: [PATCH 56/96] serve: stream GLM reasoning through reasoning_content on the OpenAI endpoint (#597 item 4) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit On /v1/chat/completions, GLM's reasoning was not separated from the answer: with tools it was silently dropped (parse_tool_calls strips everything before ), without tools the raw reasoning AND the literal were streamed as ordinary `content`. Only the Anthropic endpoint and Inkling split it. (My earlier note on #597 that non-streaming was already handled was wrong — it was handled only for Anthropic/Inkling, not the OpenAI GLM path.) Route GLM reasoning to `reasoning_content` on both the streaming and non-streaming OpenAI chat paths, reusing the existing cross-chunk ThinkingStreamSplit: - streaming, no tools: reasoning -> reasoning_content deltas, answer -> content deltas - streaming, tools: a think splitter runs BEFORE the tool-call buffer, so reasoning goes to reasoning_content and never contaminates parse_tool_calls' input - non-streaming: split_thinking_reply populates message.reasoning_content and a clean message.content - / markers are never exposed to the client ThinkingStreamSplit gains initial_thinking (start in answer mode when thinking is off, so a marker-free answer isn't filed as reasoning) and a close() alias for the streaming path; enable_thinking is plumbed from chat_completion into generation(). Tests: 5 unit cases on the splitter (single-chunk, close-tag split across chunks, stray open marker, thinking-off all-answer, missing close tag) + 5 end-to-end HTTP cases (streaming split, cross-chunk , thinking-off, reasoning-out-of-tool-args, non-streaming split). Full server suite 78/78. Co-Authored-By: Claude Opus 4.8 (1M context) --- c/openai_server.py | 56 ++++++++++--- c/tests/test_openai_server.py | 146 +++++++++++++++++++++++++++++++++- 2 files changed, 187 insertions(+), 15 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 91373088..e5b87c29 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -614,11 +614,14 @@ class ThinkingStreamSplit: """Split GLM's reasoning marker without leaking markers across stream chunks.""" MARKERS = (THINK_OPEN, THINK_CLOSE) - def __init__(self, on_thinking, on_text, on_thinking_end=None): + def __init__(self, on_thinking, on_text, on_thinking_end=None, initial_thinking=True): self.on_thinking = on_thinking self.on_text = on_text self.on_thinking_end = on_thinking_end - self.thinking = True + # #597: GLM emits reasoning only when the prompt opened (thinking on); + # with thinking off the prompt already closed it, so output is pure answer and + # the splitter must start in text mode or it would file the whole answer as reasoning. + self.thinking = initial_thinking self.buf = "" def _emit(self, text): @@ -654,11 +657,13 @@ def finish(self): self._emit(self.buf) self.buf = "" + close = finish # interface parity with InklingStreamSplit in the streaming path -def split_thinking_reply(text): + +def split_thinking_reply(text, enable_thinking=True): """Return the marker-free (thinking, answer) portions of one GLM reply.""" thinking, answer = [], [] - split = ThinkingStreamSplit(thinking.append, answer.append) + split = ThinkingStreamSplit(thinking.append, answer.append, initial_thinking=enable_thinking) split.feed(text) split.finish() return "".join(thinking), "".join(answer) @@ -1546,7 +1551,8 @@ def error_body(self, error): return error_object(error) return {"type": "error", "error": {"type": error.error_type, "message": error.message}} - def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=None): + def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=None, + enable_thinking=False): # COLI_DEBUG tees the engine transaction to stderr: 1 = decoded output stream only, # 2 = both sides (rendered prompt + output). render_chat already folds prior turns and # tool results into `prompt`, so level 2 is the full conversation the engine saw. @@ -1600,6 +1606,11 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non reasoning = "" if ARCH == "inkling": text, reasoning = split_inkling(text) + elif chat: + # #597 item 4: GLM emits reasoning then then the answer. Route the + # reasoning to reasoning_content instead of dumping it (or the raw ) + # into the visible answer / tool-call parser. + reasoning, text = split_thinking_reply(text, enable_thinking) length_finish = "length" if stats["length_limited"] else "stop" if chat and tools: content, calls = parse_tool_calls(text, tools) @@ -1694,6 +1705,10 @@ def emit_reasoning(text): # thinking → reasoning_content deltas (chat only splitter = (InklingStreamSplit(emit, emit_reasoning if chat else None) if ARCH == "inkling" else None) + # #597 item 4: GLM (chat) streams reasoning then then the answer. Split the + # reasoning into reasoning_content deltas instead of leaking it — and the raw — + # into visible content or the tool-call buffer. + glm_think = chat and ARCH != "inkling" ka_thread = threading.Thread(target=_keepalive, daemon=True) ka_thread.start() @@ -1704,10 +1719,8 @@ def emit_reasoning(text): # thinking → reasoning_content deltas (chat only sp = {"buf": "", "tool": False} hold = len(BOX_START) - 1 raw = [] - def emit_tools(chunk): + def feed_content(chunk): # answer text only (post-) raw.append(chunk) - if dbg_echo: - sys.stderr.write(chunk); sys.stderr.flush() if sp["tool"]: return sp["buf"] += chunk @@ -1722,11 +1735,22 @@ def emit_tools(chunk): if flush: emit(sp["buf"][:flush]) sp["buf"] = sp["buf"][flush:] + # #597: keep GLM reasoning out of the tool-call buffer — a think splitter sends it + # to reasoning_content and passes only the answer text on to feed_content/parser. + think = (ThinkingStreamSplit(emit_reasoning, feed_content, + initial_thinking=enable_thinking) + if glm_think else None) + def emit_tools(chunk): + if dbg_echo: + sys.stderr.write(chunk); sys.stderr.flush() + (think.feed if think else feed_content)(chunk) stop_filter = StopFilter(stop_sequences, emit_tools, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) stop_filter.finish() + if think: + think.finish() if not sp["tool"] and sp["buf"]: emit(sp["buf"]) # no tool call happened: flush held tail _content, calls = parse_tool_calls("".join(raw), tools) @@ -1737,17 +1761,24 @@ def emit_tools(chunk): "logprobs": None, "finish_reason": None}]) finish = "tool_calls" if calls else ("length" if stats["length_limited"] else "stop") else: + if splitter is not None: # inkling content/marker splitter + content_split = splitter + elif glm_think: # GLM reasoning → reasoning_content + content_split = ThinkingStreamSplit(emit_reasoning, emit, + initial_thinking=enable_thinking) + else: + content_split = None def emit_plain(chunk): if dbg_echo: sys.stderr.write(chunk); sys.stderr.flush() - (splitter.feed if splitter else emit)(chunk) + (content_split.feed if content_split else emit)(chunk) stop_filter = StopFilter(stop_sequences, emit_plain, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) stop_filter.finish() - if splitter: - splitter.close() + if content_split: + content_split.close() finish = "length" if stats["length_limited"] else "stop" ka_stop.set() # generation done: stop the keepalive pump ka_thread.join(timeout=2) @@ -1803,7 +1834,8 @@ def chat_completion(self, body, request_id): renderer = render_chat_inkling if ARCH == "inkling" else render_chat prompt = renderer(body.get("messages"), enable_thinking, reasoning_effort, tools, tool_choice) - self.generation(body, prompt, request_id, True, tools, tool_choice) + self.generation(body, prompt, request_id, True, tools, tool_choice, + enable_thinking=enable_thinking) # ---- Anthropic /v1/messages (#343) ---------------------------------------------------- ANTHROPIC_STOP = {"stop": "end_turn", "length": "max_tokens", "tool_calls": "tool_use"} diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index 3bba88d0..ab92acb4 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -13,9 +13,9 @@ from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, DEFAULT_CHAT_STOP_SEQUENCES, END, GenerationScheduler, - READY, Engine, InklingStreamSplit, StopFilter, _engine_error, - generation_options, parse_tool_calls, read_engine_turn, render_chat, - serve, stop_policy) + READY, Engine, InklingStreamSplit, StopFilter, ThinkingStreamSplit, + _engine_error, generation_options, parse_tool_calls, read_engine_turn, + render_chat, serve, split_thinking_reply, stop_policy) class FakeEngine: @@ -1005,5 +1005,145 @@ def test_untrusted_host_still_rejected_with_allowlist(self): self.assertEqual(self._get_models(server.server_port, "evil.example.com"), 403) +class ThinkingSplitUnitTest(unittest.TestCase): + """#597 item 4: the GLM reasoning splitter, incl. mkelcb's cross-chunk cases.""" + + def test_single_chunk(self): + self.assertEqual(split_thinking_reply("abcdef"), ("abc", "def")) + + def test_close_tag_split_across_chunks(self): + thinking, answer = [], [] + s = ThinkingStreamSplit(thinking.append, answer.append) + s.feed("abcdef"); s.finish() + self.assertEqual(("".join(thinking), "".join(answer)), ("abc", "def")) + + def test_open_tag_split_and_stray_open_marker(self): + thinking, answer = [], [] + s = ThinkingStreamSplit(thinking.append, answer.append) + s.feed("abcdefghi"); s.finish() + self.assertEqual(("".join(thinking), "".join(answer)), ("abcdef", "ghi")) + + def test_thinking_disabled_is_all_answer(self): + # initial_thinking=False: a pure answer with no markers must not be filed as reasoning + self.assertEqual(split_thinking_reply("plain answer", enable_thinking=False), + ("", "plain answer")) + + def test_missing_close_tag_surfaces_reasoning(self): + self.assertEqual(split_thinking_reply("thought with no end"), + ("thought with no end", "")) + + +class _ChunkEngine(FakeEngine): + """Engine that emits a caller-supplied chunk sequence, to exercise the streaming + reasoning splitter across arbitrary chunk boundaries.""" + def __init__(self, chunks): + super().__init__() + self.chunks = list(chunks) + + def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, + cancelled=None, grammar=None, stopped=None): + self.calls.append((prompt, maximum, temperature, top_p, cache_slot, grammar)) + for chunk in self.chunks: + on_text(chunk) + if stopped and stopped(): + self.stop_requests += 1 + break + return {"prompt_tokens": 7, "completion_tokens": len(self.chunks), "length_limited": False} + + +class GlmReasoningStreamTest(unittest.TestCase): + """#597 item 4 end-to-end: GLM reasoning streams as reasoning_content, the answer as + content, no / leaks, cross-chunk-safe, and reasoning never contaminates + the tool-call buffer.""" + + def _server(self, chunks): + server = APIServer(("127.0.0.1", 0), _ChunkEngine(chunks), "test-model") + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + self.addCleanup(thread.join, 2) + self.addCleanup(server.server_close) + self.addCleanup(server.shutdown) + self.addCleanup(server.scheduler.close) + return f"http://127.0.0.1:{server.server_port}" + + def _post(self, base, body): + req = Request(base + "/v1/chat/completions", + data=json.dumps(body).encode(), + headers={"Content-Type": "application/json"}) + with urlopen(req, timeout=3) as response: + return response.read().decode() + + def _deltas(self, raw): + reasoning, content, tool_calls = [], [], [] + for line in raw.splitlines(): + if not line.startswith("data: ") or line == "data: [DONE]": + continue + for choice in json.loads(line[6:])["choices"]: + delta = choice.get("delta") or {} + if delta.get("reasoning_content"): + reasoning.append(delta["reasoning_content"]) + if delta.get("content"): + content.append(delta["content"]) + if delta.get("tool_calls"): + tool_calls.extend(delta["tool_calls"]) + return "".join(reasoning), "".join(content), tool_calls + + def test_streaming_splits_reasoning_from_answer(self): + base = self._server(["I think ", "42", "", "The answer ", "is 42"]) + raw = self._post(base, {"model": "test-model", "stream": True, "enable_thinking": True, + "messages": [{"role": "user", "content": "2+2?"}]}) + reasoning, content, _ = self._deltas(raw) + self.assertEqual(reasoning, "I think 42") + self.assertEqual(content, "The answer is 42") + self.assertNotIn("", raw) + self.assertNotIn("", raw) + + def test_streaming_close_tag_split_across_chunks(self): + base = self._server(["reasonans", "wer"]) + raw = self._post(base, {"model": "test-model", "stream": True, "enable_thinking": True, + "messages": [{"role": "user", "content": "x"}]}) + reasoning, content, _ = self._deltas(raw) + self.assertEqual(reasoning, "reason") + self.assertEqual(content, "answer") + self.assertNotIn("think>", raw) + + def test_streaming_thinking_off_is_all_content(self): + base = self._server(["Just ", "the answer"]) + raw = self._post(base, {"model": "test-model", "stream": True, "enable_thinking": False, + "messages": [{"role": "user", "content": "x"}]}) + reasoning, content, _ = self._deltas(raw) + self.assertEqual(reasoning, "") + self.assertEqual(content, "Just the answer") + + def test_streaming_reasoning_stays_out_of_tool_call(self): + base = self._server(["deciding to call", "", + "get_weathercity" + "Paris"]) + raw = self._post(base, {"model": "test-model", "stream": True, "enable_thinking": True, + "messages": [{"role": "user", "content": "weather?"}], + "tools": [{"type": "function", "function": { + "name": "get_weather", "parameters": {"type": "object", + "properties": {"city": {"type": "string"}}}}}]}) + reasoning, content, tool_calls = self._deltas(raw) + self.assertEqual(reasoning, "deciding to call") + self.assertTrue(tool_calls, "expected a parsed tool call") + args = tool_calls[0]["function"]["arguments"] + self.assertIn("Paris", args) + self.assertNotIn("deciding", args) # reasoning must not leak into the tool arguments + self.assertNotIn("deciding", content) # nor into the visible answer + + def test_nonstreaming_splits_reasoning(self): + base = self._server(["mulling ", "it over", "", "final ", "answer"]) + req = Request(base + "/v1/chat/completions", + data=json.dumps({"model": "test-model", "enable_thinking": True, + "messages": [{"role": "user", "content": "x"}]}).encode(), + headers={"Content-Type": "application/json"}) + with urlopen(req, timeout=3) as response: + body = json.load(response) + message = body["choices"][0]["message"] + self.assertEqual(message["reasoning_content"], "mulling it over") + self.assertEqual(message["content"], "final answer") + + if __name__ == "__main__": unittest.main() From 32aa34f52cb6febcb63d7bdb277ff436ab490a41 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Sat, 25 Jul 2026 19:02:49 +0800 Subject: [PATCH 57/96] nix: fix flake build broken by the glm -> colibri rename The installPhase copied c/glm, but `make glm` is a phony alias for the colibri target and the link step emits c/colibri, so packages.default failed in installPhase with "cp: cannot stat 'c/glm'". apps.glm was a second casualty of the same rename: it pointed at share/colibri/glm, a path the installPhase never produced. Install the engine under its real name and let c/coli's own path resolution find it -- HERE/colibri is its first non-env candidate, so the bundled layout now matches a source checkout instead of relying on COLI_ENGINE to paper over the mismatch. Build via `make -C c colibri`, matching ci.yml, release.yml and docker/Dockerfile.slim. apps.glm becomes apps.engine ($out/bin/colibri); it is not named colibri so it doesn't shadow packages.colibri, whose mainProgram is coli. Nothing references the old attribute -- it never resolved. The devShell hints pointed at c/glm as well. Fixes #595 --- flake.nix | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/flake.nix b/flake.nix index d67aa1ce..10a61293 100644 --- a/flake.nix +++ b/flake.nix @@ -55,7 +55,7 @@ buildPhase = '' runHook preBuild - make -C c glm ARCH="$ARCH" + make -C c colibri ARCH="$ARCH" runHook postBuild ''; @@ -68,7 +68,7 @@ # modules it imports (openai_server.py, resource_plan.py, # doctor.py), and tools/ all sit next to each other. mkdir -p $out/lib/colibri/tools $out/bin - cp c/glm $out/lib/colibri/glm + cp c/colibri $out/lib/colibri/colibri cp c/coli $out/lib/colibri/coli chmod +x $out/lib/colibri/coli cp c/openai_server.py c/resource_plan.py c/doctor.py c/version.py \ @@ -76,14 +76,14 @@ cp -r c/tools/* $out/lib/colibri/tools/ # $out/bin holds the user-facing entry points. - ln -s ../lib/colibri/glm $out/bin/glm + ln -s ../lib/colibri/colibri $out/bin/colibri # Wrap coli: point it at the bundled engine (COLI_ENGINE) so it is # found by default, and at the module dir (PYTHONPATH) so # `import openai_server` / `resource_plan` / `doctor` resolve. makeWrapper ${pythonEnv}/bin/python $out/bin/coli \ --add-flags "$out/lib/colibri/coli" \ - --set-default COLI_ENGINE "$out/lib/colibri/glm" \ + --set-default COLI_ENGINE "$out/lib/colibri/colibri" \ --set PYTHONPATH "$out/lib/colibri:${pythonEnv}/${pkgs.python3.sitePackages}" runHook postInstall ''; @@ -117,9 +117,14 @@ type = "app"; program = pkgs.lib.getExe colibri; }; - glm = { + # `nix run .#engine` runs the engine binary directly, skipping the + # coli launcher. Named "engine", not "colibri", so it doesn't shadow + # packages.colibri (whose mainProgram is coli). Replaces the old + # `.#glm`, which pointed at a share/colibri/ path the installPhase + # never produced (#595). + engine = { type = "app"; - program = "${colibri}/share/colibri/glm"; + program = "${colibri}/bin/colibri"; }; }; @@ -141,9 +146,9 @@ echo " gcc: $(gcc --version | head -1)" echo " python: $(python3 --version)" echo "" - echo "Build the engine: make -C c glm" + echo "Build the engine: make -C c colibri" echo "Run the converter: python c/coli convert --model /path/to/glm52_i4" - echo "Chat: COLI_MODEL=/path/to/glm52_i4 ./c/glm ..." + echo "Chat: COLI_MODEL=/path/to/glm52_i4 ./c/colibri ..." ''; }; } From b4244f1bbc03bad9cfdae2322542c9d0fdddb074 Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sat, 25 Jul 2026 13:12:15 +0200 Subject: [PATCH 58/96] serve: ACCEPT frame so an oversized prompt is a clean 400, not a broken 200 stream (#597 item 6) A streaming chat request committed its HTTP 200 (SSE headers) before the engine tokenised the prompt and checked it against CTX. An over-long prompt was only then found -- ERROR CONTEXT_EXCEEDED -- far too late to send the 400 the client needs, so the client saw a broken/empty stream instead of a clean context_length_exceeded. (Non-streaming was already correct: it sends its 200 only after generation.) Add an acceptance frame to the engine<->gateway protocol (mkelcb's design in #597): - c/colibri.c mux_submit(): after all cheap validation (encode, non-empty, fits CTX) and BEFORE prefill, emit `ACCEPT `. Every early failure already returns first with an ERROR, so a request yields exactly one of ACCEPT or an early ERROR. - Engine._dispatch_stdout(): parse ACCEPT -> ("accept", {...}) without popping pending. - Engine.generate(): new on_accept callback; commit exactly once on the first of ACCEPT / DATA / DONE. DATA/DONE before an ACCEPT still commit (an older engine with no ACCEPT frame keeps working); a second ACCEPT is a protocol error. - APIHandler.generation() streaming: defer the SSE 200 + keepalive into start_stream(), fired via on_accept. An ERROR that arrives before ACCEPT now propagates as an APIError with nothing committed, so do_POST returns a normal JSON 400. Tests: 4 Engine-level cases over a fake engine process (ACCEPT-before-DATA fires on_accept first; ERROR-before-ACCEPT raises 400 and never commits; DATA-before-ACCEPT still commits for an old engine; duplicate ACCEPT is a protocol error) + 1 end-to-end HTTP case (streaming context-exceeded returns a clean 400 with context_length_exceeded, not a 200 stream). Full server suite 83/83; the C engine builds clean. Completes the #597 series (items 1/2/4/5 already on dev via #535/#606/#607). Co-Authored-By: Claude Opus 4.8 (1M context) --- c/colibri.c | 5 ++ c/openai_server.py | 97 ++++++++++++++++++++++-------- c/tests/test_openai_server.py | 110 ++++++++++++++++++++++++++++++++-- 3 files changed, 183 insertions(+), 29 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index bb5f7aae..3b0f1371 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -5562,6 +5562,11 @@ static int mux_submit(Model *m, Tok *T, ServeCtx *ctx, ServeReq *req, GrDraft *g printf("ERROR %llu CONTEXT_EXCEEDED %d %d\n",sub.id,nt,maxctx-2); fflush(stdout); return 0; } + /* #597: the submission is valid (encoded, non-empty, fits the context). Announce it before + * prefill so the gateway commits the streaming 200 only now. Every failure above returned + * first with an ERROR, so a request yields exactly one of ACCEPT or an early ERROR -- which + * lets a CONTEXT_EXCEEDED become a clean HTTP 400 instead of a broken already-200 stream. */ + printf("ACCEPT %llu %d\n",sub.id,nt); fflush(stdout); int prefix=0; while(prefixlen && prefixhist[prefix]==tmp[prefix]) prefix++; if(prefixlen){ sc->len=prefix; if(m->has_mtp) m->kv_start[m->c.n_layers]=-1; kv_disk_truncate(m,sc->len); } diff --git a/c/openai_server.py b/c/openai_server.py index e5b87c29..84a26574 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -1163,6 +1163,15 @@ def _dispatch_stdout(self): events = self.pending.get(request_id) if events is not None: events.put(("data", data)) + elif kind == "ACCEPT" and len(fields) >= 3: + # #597: the engine validated the submission (fits context) before prefill. + # Keep it pending — DATA/DONE still follow — and let generate() commit the + # HTTP stream only now, so an earlier CONTEXT_EXCEEDED stays a clean 400. + request_id = fields[1] + with self.pending_lock: + events = self.pending.get(request_id) + if events is not None: + events.put(("accept", {"prompt_tokens": int(fields[2])})) elif kind == "DONE" and len(fields) >= 7: request_id = fields[1] stats = self._stats(fields[2:]) @@ -1215,7 +1224,7 @@ def _dispatch_stdout(self): self._fail_pending(error) def generate(self, prompt, max_tokens, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None, stopped=None): + cancelled=None, grammar=None, stopped=None, on_accept=None): if isinstance(cache_slot, bool) or not isinstance(cache_slot, int) or not 0 <= cache_slot < self.kv_slots: raise APIError(400, "Invalid cache slot.", "cache_slot") payload = prompt.encode("utf-8") @@ -1258,9 +1267,27 @@ def decode(data): cancel_sent = False stop_sent = False + accepted = False + + def _accept(info): + # #597: commit exactly once, on the first of ACCEPT / DATA / DONE. A new engine sends + # ACCEPT before any output, so on_accept fires before prefill and a preceding + # CONTEXT_EXCEEDED never reaches here (it propagates as a 400 with nothing committed). + # An older engine that never sends ACCEPT still commits on its first DATA/DONE. + nonlocal accepted + if not accepted: + accepted = True + if on_accept is not None: + on_accept(info) + while True: kind, value = events.get() - if kind == "data": + if kind == "accept": + if accepted: + raise RuntimeError("engine sent a duplicate ACCEPT frame") + _accept(value) + elif kind == "data": + _accept({"prompt_tokens": None}) if not cancel_sent and not stop_sent: decode(value) if stopped and stopped(): @@ -1274,6 +1301,7 @@ def decode(data): self.process.stdin.write(f"CANCEL {request_id}\n".encode()) self.process.stdin.flush() elif kind == "done": + _accept({"prompt_tokens": None}) tail = decoder.decode(b"", final=True) if tail: on_text(tail) @@ -1634,22 +1662,19 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non return stream_object = "chat.completion.chunk" if chat else object_name - self.send_response(200) - self.send_header("Content-Type", "text/event-stream") - self.send_header("Cache-Control", "no-cache") - self.send_header("X-Accel-Buffering", "no") - self.send_header("x-request-id", request_id) - for name, value in queue_headers.items(): self.send_header(name, value) - self.send_cors_headers() - self.end_headers() - connected = True + # #597 item 6: DO NOT commit the 200 yet. The engine validates the prompt against the + # context AFTER we would have sent headers, so an oversized prompt used to be a + # CONTEXT_EXCEEDED discovered too late to send a clean 400. Defer the SSE headers into + # start_stream(), fired on the engine's ACCEPT frame (before prefill); an ERROR that + # arrives before ACCEPT propagates as an APIError with nothing committed -> proper 400. + connected = False + stream_started = [False] + ka_thread = [None] # KEEPALIVE: engine.generate() blocks SILENTLY during the (minutes-long) cold # prefill, and the client drops the socket after its idle timeout. A background pump - # emits a reasoning_content "." delta (the channel that reliably resets the client's - # timer and lands in the thinking panel, so answer content stays clean) whenever no - # event has been written for KA_GAP seconds. All wfile writes share ka_lock so the - # pump and event() never interleave; last_write gates the pump so it stays quiet - # while real tokens are flowing (e.g. during decode). + # emits a keepalive delta whenever no event has been written for KA_GAP seconds. All + # wfile writes share ka_lock so the pump and event() never interleave; last_write + # gates the pump so it stays quiet while real tokens are flowing (e.g. during decode). ka_lock = threading.Lock() last_write = [time.time()] ka_stop = threading.Event() @@ -1689,10 +1714,6 @@ def _keepalive(): if time.time() - last_write[0] >= KA_GAP: event(ping) - if chat: - event([{"index": 0, "delta": {"role": "assistant", "content": ""}, - "logprobs": None, "finish_reason": None}]) - def emit(text): choice = ({"index": 0, "delta": {"content": text}, "logprobs": None, "finish_reason": None} if chat else @@ -1710,8 +1731,29 @@ def emit_reasoning(text): # thinking → reasoning_content deltas (chat only # into visible content or the tool-call buffer. glm_think = chat and ARCH != "inkling" - ka_thread = threading.Thread(target=_keepalive, daemon=True) - ka_thread.start() + def start_stream(_accept_info=None): + # #597 item 6: commit the streaming 200 (and start the keepalive) exactly once, + # only after the engine ACCEPTs the prompt. Idempotent: generate() also calls this + # on the first DATA/DONE so an older engine with no ACCEPT frame still streams. + nonlocal connected + if stream_started[0]: + return + stream_started[0] = True + self.send_response(200) + self.send_header("Content-Type", "text/event-stream") + self.send_header("Cache-Control", "no-cache") + self.send_header("X-Accel-Buffering", "no") + self.send_header("x-request-id", request_id) + for name, value in queue_headers.items(): self.send_header(name, value) + self.send_cors_headers() + self.end_headers() + connected = True + last_write[0] = time.time() + if chat: + event([{"index": 0, "delta": {"role": "assistant", "content": ""}, + "logprobs": None, "finish_reason": None}]) + ka_thread[0] = threading.Thread(target=_keepalive, daemon=True) + ka_thread[0].start() if chat and tools: # Suppress tool-call markers from the streamed content and parse the authoritative # calls from the FULL reply after generation. Hold back a marker-length tail so a @@ -1747,7 +1789,8 @@ def emit_tools(chunk): stop_filter = StopFilter(stop_sequences, emit_tools, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, - lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) + lambda: not connected, grammar=grammar, stopped=stop_filter.stopped, + on_accept=start_stream) stop_filter.finish() if think: think.finish() @@ -1775,13 +1818,17 @@ def emit_plain(chunk): stop_filter = StopFilter(stop_sequences, emit_plain, ignore_leading_stop) stats = self.server.engine.generate( prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, - lambda: not connected, grammar=grammar, stopped=stop_filter.stopped) + lambda: not connected, grammar=grammar, stopped=stop_filter.stopped, + on_accept=start_stream) stop_filter.finish() if content_split: content_split.close() finish = "length" if stats["length_limited"] else "stop" + # generate() returned, so the prompt was ACCEPTed and start_stream() ran; guard anyway. + start_stream() ka_stop.set() # generation done: stop the keepalive pump - ka_thread.join(timeout=2) + if ka_thread[0] is not None: + ka_thread[0].join(timeout=2) final_choice = ({"index": 0, "delta": {}, "logprobs": None, "finish_reason": finish} if chat else {"index": 0, "text": "", "logprobs": None, "finish_reason": finish}) diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index ab92acb4..6cdf3cb9 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -24,8 +24,10 @@ def __init__(self): self.stop_requests = 0 def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None, stopped=None): + cancelled=None, grammar=None, stopped=None, on_accept=None): self.calls.append((prompt, maximum, temperature, top_p, cache_slot, grammar)) + if on_accept is not None: # simulate the engine's ACCEPT frame (#597) + on_accept({"prompt_tokens": 7}) for chunk in ("Hé", "llo"): on_text(chunk) if stopped and stopped(): @@ -41,11 +43,11 @@ def __init__(self): self.release = threading.Event() def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None, stopped=None): + cancelled=None, grammar=None, stopped=None, on_accept=None): self.entered.set() self.release.wait(2) return super().generate(prompt, maximum, temperature, top_p, on_text, cache_slot, - cancelled, grammar, stopped) + cancelled, grammar, stopped, on_accept) class TemplateTest(unittest.TestCase): @@ -1041,8 +1043,10 @@ def __init__(self, chunks): self.chunks = list(chunks) def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None, stopped=None): + cancelled=None, grammar=None, stopped=None, on_accept=None): self.calls.append((prompt, maximum, temperature, top_p, cache_slot, grammar)) + if on_accept is not None: # simulate the engine's ACCEPT frame (#597) + on_accept({"prompt_tokens": 7}) for chunk in self.chunks: on_text(chunk) if stopped and stopped(): @@ -1145,5 +1149,103 @@ def test_nonstreaming_splits_reasoning(self): self.assertEqual(message["content"], "final answer") +class AcceptFrameTest(unittest.TestCase): + """#597 item 6: the engine's ACCEPT frame gates the HTTP commit, and its invariants.""" + + def _engine(self, respond): + process = FakeProcess(respond) + with patch("openai_server.subprocess.Popen", return_value=process): + return Engine("glm", "model") + + def test_accept_fires_before_any_data(self): + def respond(process, frame): + rid = frame.split()[1] + process.stdout.feed(b"ACCEPT " + rid + b" 42\n") + process.stdout.feed(b"DATA " + rid + b" 3\nHi!\n") + process.stdout.feed(b"DONE " + rid + b" STAT 1 2.5 0 1.0 4 0\n") + engine = self._engine(respond) + seen = [] + engine.generate("hi", 8, 0.7, 0.9, lambda t: seen.append(("text", t)), + on_accept=lambda info: seen.append(("accept", info))) + engine.close() + self.assertEqual(seen[0], ("accept", {"prompt_tokens": 42})) + self.assertEqual("".join(t for k, t in seen if k == "text"), "Hi!") + + def test_error_before_accept_never_commits(self): + def respond(process, frame): + rid = frame.split()[1] + process.stdout.feed(b"ERROR " + rid + b" CONTEXT_EXCEEDED 5000 4094\n") + engine = self._engine(respond) + accepts = [] + with self.assertRaises(APIError) as caught: + engine.generate("hi", 8, 0.7, 0.9, lambda _: None, + on_accept=lambda info: accepts.append(info)) + engine.close() + self.assertEqual(caught.exception.status, 400) + self.assertEqual(caught.exception.code, "context_length_exceeded") + self.assertEqual(accepts, []) # nothing committed -> HTTP layer can send a clean 400 + + def test_data_before_accept_still_commits_for_old_engine(self): + def respond(process, frame): + rid = frame.split()[1] + process.stdout.feed(b"DATA " + rid + b" 3\nHey\n") + process.stdout.feed(b"DONE " + rid + b" STAT 1 2.5 0 1.0 4 0\n") + engine = self._engine(respond) + accepts, chunks = [], [] + engine.generate("hi", 8, 0.7, 0.9, chunks.append, + on_accept=lambda info: accepts.append(info)) + engine.close() + self.assertEqual("".join(chunks), "Hey") + self.assertEqual(len(accepts), 1) # first DATA implies acceptance (no ACCEPT frame) + self.assertIsNone(accepts[0]["prompt_tokens"]) + + def test_duplicate_accept_is_a_protocol_error(self): + def respond(process, frame): + rid = frame.split()[1] + process.stdout.feed(b"ACCEPT " + rid + b" 10\n") + process.stdout.feed(b"ACCEPT " + rid + b" 10\n") + engine = self._engine(respond) + with self.assertRaisesRegex(RuntimeError, "duplicate ACCEPT"): + engine.generate("hi", 8, 0.7, 0.9, lambda _: None, on_accept=lambda _: None) + engine.close() + + +class _ContextExceededEngine(FakeEngine): + """Engine that rejects the prompt before ACCEPT — on_accept is never called.""" + def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, + cancelled=None, grammar=None, stopped=None, on_accept=None): + raise APIError(400, "This model's maximum context length is 4094 tokens.", + "messages", "context_length_exceeded") + + +class StreamingContextRejectTest(unittest.TestCase): + """#597 item 6: an oversized prompt on a *streaming* request must return a clean HTTP 400, + not a committed 200 SSE stream that only later discovers the overflow.""" + + def setUp(self): + self.server = APIServer(("127.0.0.1", 0), _ContextExceededEngine(), "test-model") + self.thread = threading.Thread(target=self.server.serve_forever, daemon=True) + self.thread.start() + self.base = f"http://127.0.0.1:{self.server.server_port}" + + def tearDown(self): + self.server.scheduler.close() + self.server.shutdown() + self.server.server_close() + self.thread.join(timeout=2) + + def test_streaming_context_exceeded_is_clean_400(self): + req = Request(self.base + "/v1/chat/completions", + data=json.dumps({"model": "test-model", "stream": True, + "messages": [{"role": "user", "content": "x" * 100}]}).encode(), + headers={"Content-Type": "application/json"}) + with self.assertRaises(HTTPError) as caught: + urlopen(req, timeout=3) + self.assertEqual(caught.exception.code, 400) # a real 400, not a 200 stream + body = json.load(caught.exception) + self.assertEqual(body["error"]["code"], "context_length_exceeded") + self.assertEqual(body["error"]["param"], "messages") + + if __name__ == "__main__": unittest.main() From 595c7dbc7dc30a0ecbe9d3cc1faec6ffe3d02ba6 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Sat, 25 Jul 2026 19:59:07 +0800 Subject: [PATCH 59/96] serve: fix HTTP/1.1 keep-alive desync and SSE framing (#597 item 3) The reported `Bad request syntax ('{...json body...}POST /v1/chat/...')` is a previous request's body being parsed as the next request line. Two independent causes, both reproduced against dev: 1. Early rejections return before read_json(), so the body stays in the socket and the next readline() eats it. _check_host() (403) and require_auth() (401) both precede the body read -- which is why the reporter hit this through Tailscale Serve, where every request was a 403 until the item 2 allowlist landed. A bad or over-MAX_BODY Content-Length leaks the same way. 2. The streaming 200 declared no Content-Length and no Connection: close, yet close_connection was set only after the final event. A client is told to expect a reusable connection for a response whose only boundary is the close. Worse, when generation raised after the commit, that line never ran: do_POST then wrote a complete `HTTP/1.1 500` response *into* the event stream, and left the connection advertised as reusable. Rather than teaching each early return to consume its body, drain once at the request boundary in handle_one_request() -- it covers every present and future early exit. Where the body can't be swallowed safely (chunked, unparseable or over-limit length, short read) close instead: an unreusable connection is correct, a desynchronised one is not. Both SSE paths now send Connection: close and set close_connection when the 200 is committed, not when the stream ends, so a mid-stream failure can't leave a reusable socket. send_response() is overridden as the one choke point recording that the status line is out, and _fail() uses it to stay silent instead of splicing a second response into a committed stream. Plain JSON responses keep HTTP/1.1 persistence; that is covered by a test so the fix can't quietly degrade into close-everything. 11 tests: 8 fail on dev, 3 are the no-regression guards. Full suite 197. Refs #597 --- c/openai_server.py | 85 ++++++++++++++-- c/tests/test_openai_server.py | 178 ++++++++++++++++++++++++++++++++++ 2 files changed, 256 insertions(+), 7 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 84a26574..496de3c0 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -1359,10 +1359,59 @@ class APIHandler(BaseHTTPRequestHandler): timeout = 30 # per-request socket timeout: a slowloris client that dribbles its # request line/body can't pin a worker thread (and a slot) forever server_version = "colibri" + _committed = False # status line already on the wire; reset per request below + _body_read = False # request body fully consumed, so nothing is left to drain def log_message(self, fmt, *args): sys.stderr.write("[api] %s - %s\n" % (self.address_string(), fmt % args)) + def handle_one_request(self): + """Per-request bookkeeping for HTTP/1.1 persistence (#597 item 3). + + One handler instance serves every request on a keep-alive connection, so both flags + reset here rather than in do_POST. The drain afterwards is the whole fix for the + reported `Bad request syntax ('{...json...}POST /v1/...')`: any early rejection -- + 403 Host, 401 auth, a bad or oversized Content-Length -- returns before read_json(), + leaving the body in the socket, where the next readline() eats it as a request line. + Draining once at the request boundary covers every such path, present and future, + instead of asking each early return to remember.""" + self._committed = False + self._body_read = False + super().handle_one_request() + if not self.close_connection: + self._drain_request_body() + + def send_response(self, code, message=None): + """Single choke point for "the status line is out". Overriding here rather than + tracking it at each call site means no responder can forget (#597 item 3).""" + self._committed = True + super().send_response(code, message) + + def _drain_request_body(self): + """Consume any unread request body so the next request line is at the head of the + stream. Where the body can't be swallowed safely, close instead: an unreusable + connection is correct, a desynchronised one is not.""" + if self._body_read: + return + self._body_read = True + if self.headers.get("Transfer-Encoding"): + self.close_connection = True # not framed by Content-Length; we don't de-chunk + return + try: + remaining = int(self.headers.get("Content-Length", "0")) + except ValueError: + self.close_connection = True # unparseable framing: the body length is unknown + return + if remaining < 0 or remaining > MAX_BODY: + self.close_connection = True # don't burn bandwidth just to keep a socket warm + return + while remaining > 0: + chunk = self.rfile.read(min(remaining, 65536)) + if not chunk: + self.close_connection = True + return + remaining -= len(chunk) + def send_json(self, status, body, request_id=None, headers=None): data = json.dumps(body, ensure_ascii=False, separators=(",", ":")).encode() self.send_response(status) @@ -1436,8 +1485,12 @@ def read_json(self): raise APIError(400, "Invalid Content-Length header.") if length < 1 or length > MAX_BODY: raise APIError(400, f"Request body must be between 1 and {MAX_BODY} bytes.") + raw = self.rfile.read(length) + # Only a full read leaves nothing to drain; a short read means the peer went away + # mid-body, and the drain will notice the EOF and close (#597 item 3). + self._body_read = len(raw) == length try: - body = json.loads(self.rfile.read(length)) + body = json.loads(raw) except (json.JSONDecodeError, UnicodeDecodeError): raise APIError(400, "Request body must be valid JSON.") if not isinstance(body, dict): @@ -1559,20 +1612,29 @@ def do_POST(self): else: raise APIError(404, "Not found.", None, "not_found") except APIError as error: - self.send_json(error.status, self.error_body(error), request_id, error.headers) + self._fail(error, request_id) except ClientCancelled: pass except (BrokenPipeError, ConnectionResetError): pass except Exception as error: self.log_error("request failed: %s", error) - api_error = APIError(500, "The colibri engine failed to process the request.", - None, "engine_error", "server_error") try: - self.send_json(500, self.error_body(api_error), request_id) + self._fail(APIError(500, "The colibri engine failed to process the request.", + None, "engine_error", "server_error"), request_id) except OSError: pass + def _fail(self, error, request_id): + """Report an error, unless the response is already on the wire. Once a streaming 200 + is committed, a second status line would be framed as SSE body -- clients saw a whole + `HTTP/1.1 500` spliced into the event stream. All we can still do is stop talking; the + stream ends at the close, which the 200 already announced (#597 item 3).""" + if self._committed: + self.close_connection = True + return + self.send_json(error.status, self.error_body(error), request_id, error.headers) + def error_body(self, error): """Anthropic clients parse a different error envelope; the OpenAI one is unchanged.""" if urlsplit(self.path).path != "/v1/messages": @@ -1743,6 +1805,13 @@ def start_stream(_accept_info=None): self.send_header("Content-Type", "text/event-stream") self.send_header("Cache-Control", "no-cache") self.send_header("X-Accel-Buffering", "no") + # An SSE body has neither Content-Length nor chunked framing, so end-of-message + # IS the close -- HTTP/1.1 requires us to say so, or the client waits for a + # length that never comes and then tries to reuse a socket we are about to drop. + # Set close_connection HERE, not after the last event: if generation raises once + # the 200 is out, the connection must still not be offered for reuse (#597 item 3). + self.send_header("Connection", "close") + self.close_connection = True self.send_header("x-request-id", request_id) for name, value in queue_headers.items(): self.send_header(name, value) self.send_cors_headers() @@ -1842,7 +1911,7 @@ def emit_plain(chunk): self.wfile.flush() except OSError: pass - self.close_connection = True + # close_connection was already set when the 200 was committed (#597 item 3). def client_disconnected(self): try: @@ -1976,6 +2045,8 @@ def blocks_and_stop(text, stats): self.send_header("Content-Type", "text/event-stream") self.send_header("Cache-Control", "no-cache") self.send_header("X-Accel-Buffering", "no") + self.send_header("Connection", "close") # see the OpenAI path: SSE is close-framed + self.close_connection = True self.send_header("x-request-id", request_id) for name, value in queue_headers.items(): self.send_header(name, value) @@ -2107,7 +2178,7 @@ def on_text(chunk): "delta": {"stop_reason": stop_reason, "stop_sequence": None}, "usage": {"output_tokens": stats["completion_tokens"]}}) send_event("message_stop", {"type": "message_stop"}) - self.close_connection = True + # close_connection was already set when the 200 was committed (#597 item 3). def completion(self, body, request_id): prompt = body.get("prompt") diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index 6cdf3cb9..fb8235a3 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -1247,5 +1247,183 @@ def test_streaming_context_exceeded_is_clean_400(self): self.assertEqual(body["error"]["param"], "messages") +class _ExplodingEngine(FakeEngine): + """ACCEPTs the prompt (committing the streaming 200), then dies mid-generation.""" + def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, + cancelled=None, grammar=None, stopped=None, on_accept=None): + if on_accept is not None: + on_accept({"prompt_tokens": 7}) + on_text("partial") + raise RuntimeError("engine died mid-stream") + + +class KeepAliveFramingTest(unittest.TestCase): + """#597 item 3: HTTP/1.1 persistence must not desynchronise. + + The report was `Bad request syntax ('{...json body...}POST /v1/chat/completions HTTP/1.1')` + -- a previous body being parsed as the next request line. Two independent causes: an early + rejection returning before the body is read, and a streaming 200 that neither announced + close-framing nor stopped offering the socket for reuse when generation failed.""" + + CHAT = {"model": "test-model", "messages": [{"role": "user", "content": "x"}]} + + def _server(self, engine=None, **kw): + server = APIServer(("127.0.0.1", 0), engine or FakeEngine(), "test-model", **kw) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + self.addCleanup(thread.join, 2) + self.addCleanup(server.server_close) + self.addCleanup(server.shutdown) + self.addCleanup(server.scheduler.close) + return server + + def _conn(self, server): + conn = http.client.HTTPConnection("127.0.0.1", server.server_port, timeout=3) + self.addCleanup(conn.close) + return conn + + def _post(self, conn, body=None, headers=None, path="/v1/chat/completions"): + payload = json.dumps(self.CHAT if body is None else body) + head = {"Content-Type": "application/json"} + head.update(headers or {}) + conn.request("POST", path, body=payload, headers=head) + response = conn.getresponse() + return response.status, response.read() + + def _raw(self, server, request_bytes, read=4096): + """Byte-level exchange, for assertions about framing that a client library hides.""" + sock = socket.create_connection(("127.0.0.1", server.server_port), timeout=3) + self.addCleanup(sock.close) + sock.sendall(request_bytes) + chunks = [] + try: + while True: + chunk = sock.recv(read) + if not chunk: + break # server closed: the SSE message boundary + chunks.append(chunk) + except socket.timeout: + chunks.append(b"") # no EOF: the connection was left reusable + return b"".join(chunks).decode("utf-8", "replace") + + def _request_bytes(self, body, host="127.0.0.1"): + payload = json.dumps(body) + return (f"POST /v1/chat/completions HTTP/1.1\r\nHost: {host}\r\n" + f"Content-Type: application/json\r\nContent-Length: {len(payload)}\r\n" + f"\r\n{payload}").encode() + + # --- an early rejection must not leave its body in the socket ------------------------- + + def test_rejected_host_does_not_desync_the_next_request(self): + server = self._server() + conn = self._conn(server) + status, _ = self._post(conn, headers={"Host": "evil.example.com"}) + self.assertEqual(status, 403) + status, payload = self._post(conn) # same connection, valid request + self.assertEqual(status, 200, "the 403's unread body desynchronised the connection") + self.assertEqual(json.loads(payload)["object"], "chat.completion") + + def test_rejected_auth_does_not_desync_the_next_request(self): + server = self._server(api_key="secret") + conn = self._conn(server) + status, _ = self._post(conn) # no Authorization header + self.assertEqual(status, 401) + status, payload = self._post(conn, headers={"Authorization": "Bearer secret"}) + self.assertEqual(status, 200, "the 401's unread body desynchronised the connection") + self.assertEqual(json.loads(payload)["object"], "chat.completion") + + def test_unknown_model_does_not_desync_the_next_request(self): + server = self._server() + conn = self._conn(server) + status, _ = self._post(conn, body=dict(self.CHAT, model="nope")) + self.assertEqual(status, 404) + status, _ = self._post(conn) + self.assertEqual(status, 200) + + def test_each_body_is_consumed_exactly_once_across_reused_requests(self): + """The engine sees one prompt per request, with no body bytes bleeding between them.""" + engine = FakeEngine() + server = self._server(engine) + conn = self._conn(server) + for index in range(4): + body = {"model": "test-model", + "messages": [{"role": "user", "content": f"question-{index}"}]} + status, _ = self._post(conn, body=body) + self.assertEqual(status, 200) + self.assertEqual(len(engine.calls), 4) + for index, call in enumerate(engine.calls): + self.assertIn(f"question-{index}", call[0]) + self.assertNotIn("question-", call[0].split(f"question-{index}")[1], + "a later body leaked into an earlier prompt") + + def test_interleaved_rejections_and_successes_stay_in_sync(self): + server = self._server(api_key="secret") + conn = self._conn(server) + good = {"Authorization": "Bearer secret"} + for _ in range(3): + self.assertEqual(self._post(conn)[0], 401) + self.assertEqual(self._post(conn, headers={"Host": "evil.example.com", **good})[0], 403) + self.assertEqual(self._post(conn, headers=good)[0], 200) + + # --- bodies we refuse to swallow must close rather than desynchronise ----------------- + + def test_oversized_content_length_closes_instead_of_desyncing(self): + server = self._server() + raw = self._raw(server, (b"POST /v1/chat/completions HTTP/1.1\r\nHost: 127.0.0.1\r\n" + b"Content-Type: application/json\r\n" + b"Content-Length: 99999999\r\n\r\n{}")) + self.assertIn(" 400 ", raw.splitlines()[0]) + self.assertNotIn("", raw, + "an over-limit body must close the connection, not keep it alive") + + def test_unparseable_content_length_closes_instead_of_desyncing(self): + server = self._server() + raw = self._raw(server, (b"POST /v1/chat/completions HTTP/1.1\r\nHost: 127.0.0.1\r\n" + b"Content-Type: application/json\r\n" + b"Content-Length: abc\r\n\r\n{}")) + self.assertIn("400", raw.splitlines()[0]) + self.assertNotIn("", raw) + + # --- a streaming 200 is close-framed, and says so ------------------------------------ + + def test_streaming_response_announces_close_framing(self): + server = self._server() + raw = self._raw(server, self._request_bytes(dict(self.CHAT, stream=True))) + headers = raw.split("\r\n\r\n", 1)[0].lower() + self.assertIn("content-type: text/event-stream", headers) + self.assertIn("connection: close", headers, + "SSE has no Content-Length, so the close IS the boundary and must be declared") + self.assertIn("data: [DONE]", raw) + self.assertNotIn("", raw) + + def test_anthropic_stream_announces_close_framing(self): + server = self._server() + payload = json.dumps({"model": "test-model", "stream": True, "max_tokens": 16, + "messages": [{"role": "user", "content": "x"}]}) + raw = self._raw(server, (f"POST /v1/messages HTTP/1.1\r\nHost: 127.0.0.1\r\n" + f"Content-Type: application/json\r\n" + f"Content-Length: {len(payload)}\r\n\r\n{payload}").encode()) + self.assertIn("connection: close", raw.split("\r\n\r\n", 1)[0].lower()) + self.assertIn("event: message_stop", raw) + self.assertNotIn("", raw) + + def test_engine_failure_after_commit_does_not_splice_a_second_response(self): + """Once the 200 is out, a 500 status line would land inside the event stream.""" + server = self._server(_ExplodingEngine()) + raw = self._raw(server, self._request_bytes(dict(self.CHAT, stream=True))) + self.assertEqual(raw.count("HTTP/1."), 1, + "a second HTTP response was spliced into the committed SSE stream") + self.assertIn("partial", raw) # the events sent before the failure survive + self.assertNotIn("", raw) + + def test_non_streaming_response_still_reuses_the_connection(self): + """The fix must not turn every response into a close: plain JSON stays persistent.""" + server = self._server() + conn = self._conn(server) + self.assertEqual(self._post(conn)[0], 200) + self.assertEqual(self._post(conn)[0], 200) + self.assertIsNotNone(conn.sock, "the JSON path should keep the connection open") + + if __name__ == "__main__": unittest.main() From fda0071a4aa96b097da8b0e94f314d4f47fd3078 Mon Sep 17 00:00:00 2001 From: recviking Date: Fri, 24 Jul 2026 22:26:05 -0400 Subject: [PATCH 60/96] ci(hip): assert backend_cuda.o is position-independent engine-hip-syntax stayed green for the entire period `make HIP=1 colibri` could not link at all: /usr/bin/ld: backend_cuda.o: relocation R_X86_64_32 against `.rodata.str1.1' can not be used when making a PIE object; recompile with -fPIE The job never caught it because `gpu-compile` links the TEST binary with the hipcc driver, which is PIC-agnostic, and deliberately skips the gcc engine link (containerized gcc/ld cannot resolve the ROCm runtime libs). So the one link that users actually perform was the one link CI never did. #599 has since fixed the build itself by adding -fPIE to HIPCCFLAGS, so this carries no Makefile change. What is still missing is a guard: nothing fails if that flag is dropped or an explicit HIPCCFLAGS override omits it, and the failure mode is a build that CI reports as passing. Rather than fight the documented "cannot link ROCm libs in a container" constraint, assert the property the link needs -- backend_cuda.o must carry no absolute 32-bit relocations. Verified to discriminate on real objects: 713 such relocations before -fPIE, 0 after. Costs one readelf. Also installs rocwmma-dev alongside binutils, which the rocWMMA path added in #599 now requires to compile at all. Verified on ROCm 6.4.2 / gfx1201 (Radeon AI PRO R9700). --- .github/workflows/ci.yml | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b2de6e91..e7a48740 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -90,8 +90,23 @@ jobs: # link of the test binary below covers the single-source claim, and # the engine link is exercised on real ROCm hardware (GPU_BACKENDS.md). docker run --rm -v "$PWD":/src -w /src rocm/dev-ubuntu-24.04:6.2 bash -c ' - apt-get update && apt-get install -y --no-install-recommends make gcc g++ rocwmma-dev && - make -C c gpu-compile HIP=1 HIP_ARCH=gfx1100' + apt-get update && apt-get install -y --no-install-recommends make gcc g++ rocwmma-dev binutils && + make -C c gpu-compile HIP=1 HIP_ARCH=gfx1100 && + # The engine link is skipped here (see above), so assert the property + # that link needs instead: backend_cuda.o must be position-independent. + # hipcc emits a non-PIC/PIE object unless told otherwise, while gcc + # links -pie by default on the distros users build on, so absolute + # 32-bit relocations here mean `make HIP=1 colibri` dies with + # R_X86_64_32 ... can not be used when making a PIE object + # That is exactly what happened before -fPIE was added to HIPCCFLAGS: + # this job stayed green over a HIP build that could not link at all, + # because gpu-compile links the TEST binary with the hipcc driver + # (PIC-agnostic) and never runs the gcc engine link. One readelf keeps + # that regression from silently coming back. + if readelf -r c/backend_cuda.o | grep -qE "R_X86_64_32S?"; then + echo "backend_cuda.o has absolute 32-bit relocations: the PIE link of colibri will fail (HIPCCFLAGS needs -fPIE or -fPIC)" >&2 + exit 1 + fi' echo "HIP syntax check passed" windows-cuda-build: From 5eb7ee4b7bcb30b8c9f376d83409aa8df7b8586e Mon Sep 17 00:00:00 2001 From: Silios Date: Sat, 25 Jul 2026 17:09:29 +0300 Subject: [PATCH 61/96] docs(benchmarks): i5-13600K + RTX 5070 Ti datapoint - 0.98 tok/s, hit 54.7% (#605) --- docs/benchmarks.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/benchmarks.md b/docs/benchmarks.md index 09d37525..ae0b8248 100644 --- a/docs/benchmarks.md +++ b/docs/benchmarks.md @@ -101,6 +101,7 @@ Real numbers from real machines, stock build (`setup.sh`, gcc 13), greedy decodi | Ryzen AI Max+ 395 (Strix Halo, 16C/32T Zen5, avx512-vnni) · Arch Linux · 128 GB unified LPDDR5x · SK hynix P41 PCIe 4.0 ([#124](https://github.com/JustVugg/colibri/issues/124)) | — | `DIRECT=1 PIPE=1 --topp 0.7` · auto-pin | 0.06 cold → **1.10 tok/s** sustained · later **1.83 tok/s** on current dev with `DIRECT=1 PIPE=1 PILOT_REAL=1 PILOT_TWO=1` ([#200](https://github.com/JustVugg/colibri/issues/200)) | | Intel Core Ultra 9 185H (16C/22T, avx-vnni) · **native Windows 11, no WSL** · 32 GB · Crucial P3 QLC NTFS · RTX 5070 Ti ([#128](https://github.com/JustVugg/colibri/issues/128), [#273](https://github.com/JustVugg/colibri/issues/273)) | — | int8 MTP head · warm cache · GPU-resident pipeline at decode | 0.03 cold → 0.5 warm CPU → **1.07 tok/s** with the pipe2 decode gate (#274) | | Dell Pro Max GB10 (DGX Spark: Grace, **aarch64 i8mm/sve2**) · Linux · 121 GB unified LPDDR5x · GB10 sm_121 ([#136](https://github.com/JustVugg/colibri/issues/136), [#161](https://github.com/JustVugg/colibri/issues/161)) | **5.58 GB/s** O_DIRECT | int8 MTP head · warm cache | 0.50 tok/s warm · **2.4 tok/s full-k8**, **3.33 tok/s** with `CACHE_ROUTE` (#199) | +| Intel i5-13600K (14C/20T, avx-vnni) · native Linux · 62 GB · Samsung 980 PRO PCIe 4.0 (NTFS/ntfs3) · RTX 5070 Ti ([#605](https://github.com/JustVugg/colibri/issues/605)) | **5.90 GB/s** O_DIRECT | MTP off · `PIN=auto PIN_GB=20 DIRECT=1 PIPE=1 --ram 50 --cap 32 --topp 0.7` · 750 VRAM + 307 RAM pinned (5.8 GB) | **0.98 tok/s** (peak 1.07) · hit 54.7% (pin 37.9 + lru 16.7) · 360 experts/token · RSS 42.0 GB — up from 0.56 tok/s / hit 45.4% untuned | | **6 × RTX 5090 · dual Xeon Silver 4510 · 251 GB** (author's rig, [experiment log](experiments/glm52-6x5090-2026-07-12.md)) | NVMe | `CUDA_EXPERT_GB=auto PIN_GB=all` full residency · `COLI_CUDA_PIPE=2 TC_W4A16` · DRAFT=0 | **5.8–6.8 tok/s** decode · TTFT ~13 s · hit 89–100% | ### Takeaways From 3e5d68adf71e4e04da9fe06eede9ab3ae873c4fa Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Sat, 25 Jul 2026 23:06:51 +0800 Subject: [PATCH 62/96] loader: diagnose a missing core tensor instead of just naming it "missing model.norm.weight" told the user nothing about why, so #586 took three rounds of back-and-forth to land on the real cause -- a container whose tail shards never downloaded. The maintainer's reasoning there is mechanical enough for the engine to do it: the final norm and lm_head are written into the LAST shards, so a truncated transfer indexes nearly every tensor and then dies on the first one from the gap. st_die_missing() reports what was actually found -- tensors indexed, shard files read, and what the filenames say about completeness. The HF layout declares its total in `-of-NNNNN`, so a gap is stated exactly ("declare 144 shards, but only 122 are here -- 22 MISSING"); the converter's out-NNNNN layout has no total, but contiguous numbering still shows the gap must be at the tail, which is the #586 case. The conclusion follows that evidence rather than always advising a re-download: with every declared shard present it says so and asks for a bug report instead, since re-downloading could not help. Zero indexed tensors is reported as a --model path problem, which is a different failure wearing the same message. Wired into ld() and the fatal branch of the expert-tensor load. The non-fatal probe path keeps its one-line "missing" log -- it is called per expert and must not flood. Refs #586, #583 --- c/colibri.c | 5 +++-- c/st.h | 65 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+), 2 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 3b0f1371..741922a0 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -1075,7 +1075,7 @@ static QT qt_load(Model *m, const char *name, int O, int I, int bits){ return t; } static float *ld(Model *m, const char *name){ /* tensore 1D f32 residente (norme/bias) */ - int64_t n=st_numel(&m->S,name); if(n<0){fprintf(stderr,"missing %s\n",name);exit(1);} + int64_t n=st_numel(&m->S,name); if(n<0) st_die_missing(&m->S,name); float *p=(float*)qalloc((size_t)n*sizeof(float)); /* registrato per la GPU sotto METAL */ st_read_f32(&m->S,name,p,0); return p; } @@ -1485,7 +1485,8 @@ static int expert_load_impl(Model *m, int layer, int eid, ESlot *s, int fatal, i for(int k=0;k<3;k++){ tw[k]=st_find(&m->S,nm[k]); snprintf(qn,sizeof(qn),"%s.qs",nm[k]); tq[k]=st_find(&m->S,qn); - if(!tw[k]||!tq[k]){ fprintf(stderr,"missing %s\n",nm[k]); if(fatal) exit(1); return -1; } + if(!tw[k]||!tq[k]){ if(fatal) st_die_missing(&m->S,nm[k]); /* #586: diagnose, don't just name it */ + fprintf(stderr,"missing %s\n",nm[k]); return -1; } } if(g_disk_split){ /* split load/byte per tipo layer; atomici: expert_load gira anche su OMP/pipe/pilot */ int64_t tb=0; for(int k=0;k<3;k++) tb+=tw[k]->nbytes+tq[k]->nbytes; diff --git a/c/st.h b/c/st.h index 8b911d38..e61a3a38 100644 --- a/c/st.h +++ b/c/st.h @@ -377,6 +377,71 @@ static st_tensor *st_find(shards *S, const char *name) { } static int st_has(shards *S, const char *name) { return st_find(S, name) != NULL; } +/* A missing CORE tensor is almost never an engine bug: the converter writes the final + * norm and lm_head into the LAST shards, so a transfer that stopped early indexes + * nearly everything and then dies on the first tensor from the gap. Bare "missing + * model.norm.weight" gave the user no way to tell that apart from a wrong --model or a + * real bug (#586, #583). Report what was actually found, and let the filenames say + * whether shards are absent: the HF layout declares the total in `-of-NNNNN`, the + * converter layout at least reveals a truncated tail. */ +static void st_die_missing(shards *S, const char *name) { + fprintf(stderr, "missing %s\n\n", name); + if (S->nfd == 0 || S->n == 0) { + fprintf(stderr, " No safetensors tensors were indexed at all. --model must point AT the\n" + " container directory -- the one holding the *.safetensors shards.\n"); + exit(1); + } + int lo = -1, hi = -1, declared = 0, numbered = 0; + for (int i = 0; i < S->nfd; i++) { + const char *b = strrchr(S->paths[i], '/'); +#ifdef _WIN32 + const char *b2 = strrchr(S->paths[i], '\\'); + if (b2 && (!b || b2 > b)) b = b2; +#endif + b = b ? b + 1 : S->paths[i]; + int idx, tot; + if (sscanf(b, "model-%d-of-%d", &idx, &tot) == 2) declared = tot; + else if (sscanf(b, "out-%d", &idx) != 1) continue; /* out-mtp-* etc: not numbered */ + numbered++; + if (lo < 0 || idx < lo) lo = idx; + if (idx > hi) hi = idx; + } + int complete = declared > 0 && numbered >= declared; + fprintf(stderr, " indexed %d tensors from %d shard file(s)\n", S->n, S->nfd); + if (declared > 0 && numbered < declared) + fprintf(stderr, " shard filenames declare %d shards, but only %d are here -- %d MISSING\n", + declared, numbered, declared - numbered); + else if (declared > 0) + fprintf(stderr, " shard filenames declare %d shards and %d are here\n", declared, numbered); + else if (numbered > 0) + fprintf(stderr, " shards numbered %05d..%05d, %d file(s)%s\n", lo, hi, numbered, + numbered == hi - lo + 1 ? " (contiguous: a gap would be at the tail)" : " (GAPS in the numbering)"); + /* Follow the evidence: telling someone who already has every declared shard to + * re-download sends them round a loop that cannot help them. */ + if (complete) { + fprintf(stderr, + "\n '%s' is a core tensor the engine cannot run without, and every shard the\n" + " filenames declare is present -- so this is NOT the usual truncated download.\n" + " Either the container was built without this tensor, or the engine is looking\n" + " for the wrong name. Please report it with this output.\n", name); + exit(1); + } + /* The final norm and lm_head sit at the END of the model, so an interrupted transfer + * loses them first -- worth saying, but only where it's true. */ + if (strstr(name, "model.norm") || strstr(name, "lm_head")) + fprintf(stderr, "\n '%s' is written into one of the LAST shards, so a container whose tail\n" + " never arrived indexes almost everything and then fails exactly here.\n", name); + else + fprintf(stderr, "\n '%s' is a core tensor: the engine cannot run without it.\n", name); + fprintf(stderr, + " An incomplete transfer is far more likely than a corrupt engine. Re-running the\n" + " download resumes only the missing shards; the HF xet backend is known to stall\n" + " mid-transfer (#452), so disable it:\n" + " HF_HUB_DISABLE_XET=1 hf download --local-dir \n" + " If your shard count is already complete, that IS a bug worth reporting.\n"); + exit(1); +} + /* prefetch ASINCRONO: dice al kernel di iniziare a leggere le pagine del tensore in * background (readahead). Serve a sovrapporre l'I/O degli expert col calcolo: si * prefetcha tutto il set di expert di un layer, poi le pread sincrone trovano la cache From 9b2b7d15945a43c0dfc46778a635e5a6d614ed42 Mon Sep 17 00:00:00 2001 From: bill Date: Sat, 25 Jul 2026 14:18:24 -0400 Subject: [PATCH 63/96] feat: add deep doctor preflight --- README.md | 1 + c/coli | 8 +- c/doctor.py | 342 ++++++++++++++++++++++++++++++++++++++++- c/tests/test_doctor.py | 123 +++++++++++++++ docs/SETTINGS.md | 9 +- 5 files changed, 477 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 86aad79c..4b6d6922 100644 --- a/README.md +++ b/README.md @@ -259,6 +259,7 @@ the full 756 GB on disk at once: COLI_MODEL=/nvme/glm52_i4 ./coli chat # RAM budget, cache and MTP auto-detected COLI_MODEL=/nvme/glm52_i4 ./coli plan # inspect the planned VRAM/RAM/disk placement COLI_MODEL=/nvme/glm52_i4 ./coli doctor # read-only readiness check +COLI_MODEL=/nvme/glm52_i4 ./coli doctor --deep # strict tensors/shards/index/mirror preflight ./coli web --model /nvme/glm52_i4 # API + web dashboard on one port ./coli serve --model /nvme/glm52_i4 # OpenAI-compatible API only ``` diff --git a/c/coli b/c/coli index c6afcfe3..d9b13e6b 100755 --- a/c/coli +++ b/c/coli @@ -551,11 +551,15 @@ def cmd_doctor(a): if vram<0: raise ValueError("--vram cannot be negative") except ValueError as error: report={"schema_version":1,"status":"error","model":os.path.abspath(a.model), + "mode":"deep" if a.deep else "standard", "checks":[{"id":"config.arguments","status":"fail","summary":str(error)}], "plan":None} print(json.dumps(report,indent=2) if a.json else format_doctor(report)) return 2 - report=run_doctor(a.model,ram,ctx,devices,vram,engine_path=GLM) + report=run_doctor( + a.model, ram, ctx, devices, vram, engine_path=GLM, deep=a.deep, + mirror_dir=os.environ.get("COLI_MODEL_MIRROR"), + ) print(json.dumps(report,indent=2) if a.json else format_doctor(report)) return exit_code(report) @@ -984,6 +988,8 @@ def main(): pp.add_argument("--json",action="store_true") pd=sub.add_parser("doctor",parents=[common]) pd.add_argument("--json",action="store_true",help="emit a versioned JSON report") + pd.add_argument("--deep",action="store_true", + help="strictly check every tensor layout, model index, and configured mirror") pr=sub.add_parser("run", parents=[common]); pr.add_argument("prompt", nargs="*") pc=sub.add_parser("chat", parents=[common]) pc.add_argument("--attach", nargs="?", const="http://127.0.0.1:8000", default=None, diff --git a/c/doctor.py b/c/doctor.py index cbe9c870..b87db002 100644 --- a/c/doctor.py +++ b/c/doctor.py @@ -4,11 +4,26 @@ import os import sys import json +import re import subprocess from pathlib import Path from resource_plan import GB, build_plan, discover_gpus, format_plan, memory_available +SAFETENSORS_MAX_HEADER = 512 << 20 +SAFETENSORS_DTYPES = { + "BF16": 2, + "F16": 2, + "F32": 4, + "U8": 1, + "I8": 1, +} +REQUIRED_CORE_TENSORS = ( + "model.embed_tokens.weight", + "model.norm.weight", + "lm_head.weight", +) + def _check(identifier, status, summary, **details): item = {"id": identifier, "status": status, "summary": summary} @@ -17,6 +32,289 @@ def _check(identifier, status, summary, **details): return item +def _json_object(pairs): + result = {} + for key, value in pairs: + if key in result: + raise ValueError(f"duplicate JSON key: {key}") + result[key] = value + return result + + +def _safetensors_header(path): + """Read one bounded safetensors header without touching tensor payloads.""" + path = Path(path) + file_size = path.stat().st_size + with path.open("rb") as stream: + raw_length = stream.read(8) + if len(raw_length) != 8: + raise ValueError("short safetensors header") + header_length = int.from_bytes(raw_length, "little") + if (header_length < 2 or header_length > SAFETENSORS_MAX_HEADER or + header_length > file_size - 8): + raise ValueError(f"invalid safetensors header length: {header_length}") + raw_header = stream.read(header_length) + if len(raw_header) != header_length: + raise ValueError("short safetensors header body") + try: + header = json.loads(raw_header, object_pairs_hook=_json_object) + except (UnicodeDecodeError, json.JSONDecodeError) as error: + raise ValueError(f"invalid safetensors JSON: {error}") from error + if not isinstance(header, dict): + raise ValueError("safetensors header is not a JSON object") + return file_size, raw_header, header + + +def _tensor_layout(meta, payload_size): + if not isinstance(meta, dict): + raise ValueError("tensor metadata is not an object") + dtype = meta.get("dtype") + offsets = meta.get("data_offsets") + shape = meta.get("shape") + if dtype not in SAFETENSORS_DTYPES: + raise ValueError(f"unsupported dtype: {dtype!r}") + if (not isinstance(offsets, list) or len(offsets) != 2 or + any(isinstance(value, bool) or not isinstance(value, int) for value in offsets)): + raise ValueError("data_offsets must contain exactly two integers") + start, end = offsets + if start < 0 or end < start or end > payload_size: + raise ValueError(f"data_offsets [{start}, {end}] exceed payload size {payload_size}") + if (not isinstance(shape, list) or + any(isinstance(value, bool) or not isinstance(value, int) or value < 0 + for value in shape)): + raise ValueError("shape must contain only non-negative integers") + elements = 1 + for dimension in shape: + elements *= dimension + if elements > (1 << 63) - 1: + raise ValueError("shape element count exceeds int64") + if dtype not in ("U8", "I8") and end - start != elements * SAFETENSORS_DTYPES[dtype]: + raise ValueError("shape and dtype disagree with the tensor byte span") + return start, end + + +def _shard_sequence_report(shards): + hf_shards = [] + out_shards = [] + for shard in shards: + hf_match = re.fullmatch(r"model-(\d+)-of-(\d+)\.safetensors", shard.name) + out_match = re.fullmatch(r"out-(\d+)\.safetensors", shard.name) + if hf_match: + hf_shards.append(tuple(map(int, hf_match.groups()))) + elif out_match: + out_shards.append(int(out_match.group(1))) + if hf_shards: + declared = {total for _, total in hf_shards} + if len(declared) != 1: + return {"status": "fail", "summary": "shard filenames declare different totals"} + total = declared.pop() + found = {index for index, _ in hf_shards} + missing = sorted(set(range(1, total + 1)) - found) + unexpected = sorted(found - set(range(1, total + 1))) + if missing or unexpected or len(found) != len(hf_shards): + return { + "status": "fail", + "summary": "declared shard sequence is incomplete or inconsistent", + "details": { + "declared_shards": total, + "found_shards": len(found), + "missing_shards": len(missing), + "unexpected_shards": len(unexpected), + }, + } + return { + "status": "pass", + "summary": "all filename-declared shards are present", + "details": {"declared_shards": total, "found_shards": len(found)}, + } + if out_shards: + found = set(out_shards) + expected = set(range(min(found), max(found) + 1)) + missing = sorted(expected - found) + if missing or len(found) != len(out_shards): + return { + "status": "fail", + "summary": "converter shard numbering contains gaps or duplicates", + "details": { + "first_shard": min(found), + "last_shard": max(found), + "found_shards": len(found), + "missing_shards": len(missing), + "tail_completeness_declared": False, + }, + } + return { + "status": "pass", + "summary": "converter shard numbering is contiguous", + "details": { + "first_shard": min(found), + "last_shard": max(found), + "found_shards": len(found), + "tail_completeness_declared": False, + }, + } + return {"status": "skip", "summary": "shard filenames do not declare a sequence"} + + +def deep_container_report(model, mirror_dir=None): + """Validate all tensor headers/layouts and runtime-equivalent mirror admission.""" + model = Path(model).expanduser().resolve() + shards = sorted(model.glob("*.safetensors")) + if not shards: + raise ValueError("no safetensors shards found") + if len(shards) > 512: + raise ValueError("more than 512 safetensors shards are not supported by the runtime") + + tensor_sources = {} + shard_headers = {} + tensor_count = 0 + header_bytes = 0 + payload_bytes = 0 + for shard in shards: + try: + file_size, raw_header, header = _safetensors_header(shard) + except (OSError, ValueError) as error: + raise ValueError(f"{shard.name}: {error}") from error + payload_size = file_size - 8 - len(raw_header) + ranges = [] + for name, meta in header.items(): + if name == "__metadata__": + if not isinstance(meta, dict): + raise ValueError(f"{shard.name}: __metadata__ is not an object") + continue + if name in tensor_sources: + raise ValueError( + f"duplicate tensor {name!r} in {tensor_sources[name]} and {shard.name}" + ) + try: + start, end = _tensor_layout(meta, payload_size) + except ValueError as error: + raise ValueError(f"{shard.name}: tensor {name!r}: {error}") from error + tensor_sources[name] = shard.name + tensor_count += 1 + ranges.append((start, end, name)) + ranges = [item for item in ranges if item[0] != item[1]] + ranges.sort() + for previous, current in zip(ranges, ranges[1:]): + if current[0] < previous[1]: + raise ValueError( + f"{shard.name}: tensors {previous[2]!r} and {current[2]!r} overlap" + ) + shard_headers[shard.name] = (file_size, raw_header) + header_bytes += len(raw_header) + payload_bytes += payload_size + + index_path = model / "model.safetensors.index.json" + index = {"status": "skip", "summary": "model index is not present"} + if index_path.is_file(): + try: + document = json.loads(index_path.read_text(encoding="utf-8"), + object_pairs_hook=_json_object) + weight_map = document.get("weight_map") + if not isinstance(weight_map, dict): + raise ValueError("weight_map is not an object") + if any(not isinstance(name, str) or not isinstance(shard, str) + for name, shard in weight_map.items()): + raise ValueError("weight_map keys and values must be strings") + missing_tensors = sorted(set(weight_map) - set(tensor_sources)) + unindexed_tensors = sorted(set(tensor_sources) - set(weight_map)) + misplaced_tensors = sorted( + name for name in set(weight_map) & set(tensor_sources) + if weight_map[name] != tensor_sources[name] + ) + unknown_shards = sorted( + {name for name in weight_map.values() if name not in shard_headers} + ) + if missing_tensors or unindexed_tensors or misplaced_tensors or unknown_shards: + raise ValueError( + "index disagrees with scanned tensors " + f"(missing={len(missing_tensors)}, unindexed={len(unindexed_tensors)}, " + f"misplaced={len(misplaced_tensors)}, unknown_shards={len(unknown_shards)})" + ) + index = { + "status": "pass", + "summary": "model index matches every scanned tensor", + "details": {"indexed_tensors": len(weight_map)}, + } + except (OSError, UnicodeDecodeError, json.JSONDecodeError, ValueError) as error: + index = {"status": "fail", "summary": f"model index is invalid: {error}"} + + missing_core = [name for name in REQUIRED_CORE_TENSORS if name not in tensor_sources] + required = { + "status": "fail" if missing_core else "pass", + "summary": ( + f"{len(missing_core)} required core tensor(s) are missing" + if missing_core else "required core tensors are present" + ), + "details": { + "required_tensors": len(REQUIRED_CORE_TENSORS), + "missing_tensors": missing_core, + }, + } + + mirror = {"status": "skip", "summary": "no mirror directory is configured"} + if mirror_dir: + mirror_path = Path(mirror_dir).expanduser().resolve() + accepted = 0 + missing = 0 + divergent = 0 + if not mirror_path.is_dir(): + mirror = { + "status": "warn", + "summary": "configured mirror directory is unavailable", + "details": {"path": str(mirror_path), "accepted_shards": 0}, + } + else: + for name, (primary_size, primary_header) in shard_headers.items(): + candidate = mirror_path / name + if not candidate.is_file(): + missing += 1 + continue + try: + mirror_size, mirror_header, _ = _safetensors_header(candidate) + except (OSError, ValueError): + divergent += 1 + continue + if mirror_size == primary_size and mirror_header == primary_header: + accepted += 1 + else: + divergent += 1 + if divergent: + status = "warn" + summary = "one or more mirror shards would be rejected by the runtime" + elif accepted: + status = "pass" + summary = "mirror shards satisfy runtime size and header admission" + else: + status = "warn" + summary = "configured mirror contains no admissible primary shards" + mirror = { + "status": status, + "summary": summary, + "details": { + "path": str(mirror_path), + "accepted_shards": accepted, + "missing_shards": missing, + "divergent_shards": divergent, + "partial_mirror_allowed": True, + }, + } + + return { + "container": { + "shards": len(shards), + "tensors": tensor_count, + "header_bytes": header_bytes, + "payload_bytes": payload_bytes, + "payload_hashing": False, + }, + "sequence": _shard_sequence_report(shards), + "required": required, + "index": index, + "mirror": mirror, + } + + def cuda_linkage(engine_path): """Return CUDA linkage state without loading the executable or CUDA runtime.""" engine = Path(engine_path) @@ -50,7 +348,7 @@ def cuda_linkage(engine_path): def run_doctor(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0, *, engine_path, available_memory=None, available_disk=None, gpus=None, - linkage=None): + linkage=None, deep=False, mirror_dir=None): """Collect a complete report. No model payload, engine, or CUDA context is loaded.""" model = Path(model).expanduser().resolve() checks = [] @@ -150,16 +448,54 @@ def run_doctor(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0, *, checks.append(_check("placement.plan", "warn", "; ".join(plan["warnings"]))) else: checks.append(_check("placement.plan", "pass", "tier placement has no warnings")) - except (OSError, ValueError, KeyError) as error: + except (OSError, ValueError, KeyError, TypeError) as error: checks.append(_check("model.shards", "fail", str(error))) checks.append(_check("storage.disk", "skip", "storage check requires a valid model")) checks.append(_check("memory.ram", "skip", "RAM projection requires a valid model")) checks.append(_check("placement.plan", "skip", "placement requires a valid model")) + if deep: + try: + deep_report = deep_container_report(model, mirror_dir=mirror_dir) + details = deep_report["container"] + checks.append(_check( + "model.container", "pass", + "all tensor headers and layouts are internally consistent", + **details, + )) + sequence = deep_report["sequence"] + checks.append(_check( + "model.shard_sequence", sequence["status"], sequence["summary"], + **sequence.get("details", {}), + )) + required = deep_report["required"] + checks.append(_check( + "model.required", required["status"], required["summary"], + **required.get("details", {}), + )) + index = deep_report["index"] + checks.append(_check("model.index", index["status"], index["summary"], + **index.get("details", {}))) + mirror = deep_report["mirror"] + checks.append(_check("storage.mirror", mirror["status"], mirror["summary"], + **mirror.get("details", {}))) + except (OSError, ValueError) as error: + checks.append(_check("model.container", "fail", str(error))) + checks.append(_check( + "model.shard_sequence", "skip", + "shard sequence check requires a valid container", + )) + checks.append(_check( + "model.required", "skip", + "required-tensor check requires a valid container", + )) + checks.append(_check("model.index", "skip", "index check requires a valid container")) + checks.append(_check("storage.mirror", "skip", "mirror check requires a valid container")) + statuses = {item["status"] for item in checks} status = "error" if "fail" in statuses else "warning" if "warn" in statuses else "ok" return {"schema_version": 1, "status": status, "model": str(model), - "checks": checks, "plan": plan} + "mode": "deep" if deep else "standard", "checks": checks, "plan": plan} def format_doctor(report): diff --git a/c/tests/test_doctor.py b/c/tests/test_doctor.py index 89501471..aeec755d 100644 --- a/c/tests/test_doctor.py +++ b/c/tests/test_doctor.py @@ -41,6 +41,8 @@ def setUp(self): (self.model / "tokenizer.json").write_text("{}") write_shard(self.model / "model.safetensors", [ ("model.embed_tokens.weight", 100), + ("model.norm.weight", 8), + ("lm_head.weight", 100), ("model.layers.0.self_attn.q_a_proj.weight", 200), ("model.layers.1.mlp.experts.0.gate_proj.weight", 30), ("model.layers.1.mlp.experts.0.up_proj.weight", 30), @@ -79,6 +81,7 @@ def test_healthy_cpu_install_has_versioned_report(self): checks = self.checks_by_id(report) self.assertEqual(report["schema_version"], 1) + self.assertEqual(report["mode"], "standard") self.assertEqual(report["status"], "ok") self.assertIsNotNone(report["plan"]) self.assertEqual(checks["accelerator.cuda"]["status"], "skip") @@ -169,6 +172,126 @@ def test_cli_json_is_machine_readable_without_loading_model(self): self.assertTrue(run.stdout.lstrip().startswith("{")) self.assertTrue(run.stdout.rstrip().endswith("}")) + def test_deep_check_validates_every_tensor_layout(self): + report = self.report(deep=True) + checks = self.checks_by_id(report) + + self.assertEqual(report["mode"], "deep") + self.assertEqual(checks["model.container"]["status"], "pass") + self.assertEqual(checks["model.container"]["details"]["shards"], 1) + self.assertEqual(checks["model.container"]["details"]["tensors"], 8) + self.assertFalse(checks["model.container"]["details"]["payload_hashing"]) + self.assertEqual(checks["model.shard_sequence"]["status"], "skip") + self.assertEqual(checks["model.required"]["status"], "pass") + self.assertEqual(checks["model.index"]["status"], "skip") + self.assertEqual(checks["storage.mirror"]["status"], "skip") + + def test_deep_check_rejects_overlapping_tensor_ranges(self): + header = { + "first": {"dtype": "U8", "shape": [4], "data_offsets": [0, 4]}, + "second": {"dtype": "U8", "shape": [4], "data_offsets": [2, 6]}, + } + raw = json.dumps(header).encode() + shard = self.model / "model.safetensors" + shard.write_bytes(struct.pack(""` | One-shot generation for the given prompt (positional, may be multi-word). | | `chat` | Interactive REPL chat. | | `serve` | Start the OpenAI-compatible HTTP server. | @@ -74,6 +74,11 @@ Flags may also be given **after** the subcommand. Most flags map onto an engine **`bench`**: `[tasks...]` (positional), `--limit 40`, `--data `. **`plan` / `doctor`**: `--json`. +**`doctor`**: `--deep` strictly checks every safetensors header and tensor +layout, filename-declared shard completeness, required core tensors, an +optional model index, and runtime-equivalent size/header admission for +`COLI_MODEL_MIRROR`. It does not hash tensor payloads or load the engine. + --- ## `openai_server.py` — the HTTP server From b6478e9ad57eb2e67e17363741434905862ad0f6 Mon Sep 17 00:00:00 2001 From: bill Date: Sat, 25 Jul 2026 14:59:24 -0400 Subject: [PATCH 64/96] fix: harden deep doctor input validation --- c/doctor.py | 62 +++++++++++++++++++++++++++++++----------- c/tests/test_doctor.py | 55 +++++++++++++++++++++++++++++++++++++ 2 files changed, 101 insertions(+), 16 deletions(-) diff --git a/c/doctor.py b/c/doctor.py index b87db002..fa4d63c2 100644 --- a/c/doctor.py +++ b/c/doctor.py @@ -11,6 +11,8 @@ from resource_plan import GB, build_plan, discover_gpus, format_plan, memory_available SAFETENSORS_MAX_HEADER = 512 << 20 +MODEL_INDEX_MAX_BYTES = SAFETENSORS_MAX_HEADER +MAX_SAFETENSORS_SHARDS = 512 SAFETENSORS_DTYPES = { "BF16": 2, "F16": 2, @@ -44,8 +46,8 @@ def _json_object(pairs): def _safetensors_header(path): """Read one bounded safetensors header without touching tensor payloads.""" path = Path(path) - file_size = path.stat().st_size with path.open("rb") as stream: + file_size = os.fstat(stream.fileno()).st_size raw_length = stream.read(8) if len(raw_length) != 8: raise ValueError("short safetensors header") @@ -103,23 +105,35 @@ def _shard_sequence_report(shards): hf_shards.append(tuple(map(int, hf_match.groups()))) elif out_match: out_shards.append(int(out_match.group(1))) + if hf_shards and out_shards: + return { + "status": "fail", + "summary": "model mixes filename-declared shard schemes", + "details": { + "huggingface_shards": len(hf_shards), + "converter_shards": len(out_shards), + }, + } if hf_shards: declared = {total for _, total in hf_shards} if len(declared) != 1: return {"status": "fail", "summary": "shard filenames declare different totals"} total = declared.pop() found = {index for index, _ in hf_shards} - missing = sorted(set(range(1, total + 1)) - found) - unexpected = sorted(found - set(range(1, total + 1))) - if missing or unexpected or len(found) != len(hf_shards): + in_range = {index for index in found if 1 <= index <= total} + missing = max(total - len(in_range), 0) + unexpected = len(found - in_range) + duplicates = len(hf_shards) - len(found) + if missing or unexpected or duplicates: return { "status": "fail", "summary": "declared shard sequence is incomplete or inconsistent", "details": { "declared_shards": total, "found_shards": len(found), - "missing_shards": len(missing), - "unexpected_shards": len(unexpected), + "missing_shards": missing, + "unexpected_shards": unexpected, + "duplicate_shards": duplicates, }, } return { @@ -129,17 +143,20 @@ def _shard_sequence_report(shards): } if out_shards: found = set(out_shards) - expected = set(range(min(found), max(found) + 1)) - missing = sorted(expected - found) - if missing or len(found) != len(out_shards): + first = min(found) + last = max(found) + missing = last + 1 - len(found) + duplicates = len(out_shards) - len(found) + if missing or duplicates: return { "status": "fail", "summary": "converter shard numbering contains gaps or duplicates", "details": { - "first_shard": min(found), - "last_shard": max(found), + "first_shard": first, + "last_shard": last, "found_shards": len(found), - "missing_shards": len(missing), + "missing_shards": missing, + "duplicate_shards": duplicates, "tail_completeness_declared": False, }, } @@ -162,8 +179,11 @@ def deep_container_report(model, mirror_dir=None): shards = sorted(model.glob("*.safetensors")) if not shards: raise ValueError("no safetensors shards found") - if len(shards) > 512: - raise ValueError("more than 512 safetensors shards are not supported by the runtime") + if len(shards) > MAX_SAFETENSORS_SHARDS: + raise ValueError( + f"more than {MAX_SAFETENSORS_SHARDS} safetensors shards " + "are not supported by the runtime" + ) tensor_sources = {} shard_headers = {} @@ -208,8 +228,18 @@ def deep_container_report(model, mirror_dir=None): index = {"status": "skip", "summary": "model index is not present"} if index_path.is_file(): try: - document = json.loads(index_path.read_text(encoding="utf-8"), - object_pairs_hook=_json_object) + with index_path.open("rb") as stream: + index_size = os.fstat(stream.fileno()).st_size + if index_size > MODEL_INDEX_MAX_BYTES: + raise ValueError( + f"model index exceeds {MODEL_INDEX_MAX_BYTES} bytes" + ) + raw_index = stream.read(index_size + 1) + if len(raw_index) != index_size: + raise ValueError("model index changed while reading") + document = json.loads(raw_index, object_pairs_hook=_json_object) + if not isinstance(document, dict): + raise ValueError("model index is not a JSON object") weight_map = document.get("weight_map") if not isinstance(weight_map, dict): raise ValueError("weight_map is not an object") diff --git a/c/tests/test_doctor.py b/c/tests/test_doctor.py index aeec755d..958e949f 100644 --- a/c/tests/test_doctor.py +++ b/c/tests/test_doctor.py @@ -5,6 +5,7 @@ import tempfile import unittest from pathlib import Path +from unittest import mock from doctor import exit_code, format_doctor, run_doctor from resource_plan import GB @@ -220,6 +221,41 @@ def test_deep_check_rejects_gap_in_converter_shard_sequence(self): self.assertEqual(checks["model.shard_sequence"]["status"], "fail") self.assertEqual(checks["model.shard_sequence"]["details"]["missing_shards"], 1) + def test_deep_check_rejects_converter_sequence_not_starting_at_zero(self): + write_shard(self.model / "out-00005.safetensors", [("five.weight", 1)]) + write_shard(self.model / "out-00006.safetensors", [("six.weight", 1)]) + + checks = self.checks_by_id(self.report(deep=True)) + + self.assertEqual(checks["model.container"]["status"], "pass") + self.assertEqual(checks["model.shard_sequence"]["status"], "fail") + self.assertEqual(checks["model.shard_sequence"]["details"]["first_shard"], 5) + self.assertEqual(checks["model.shard_sequence"]["details"]["missing_shards"], 5) + + def test_deep_check_handles_sparse_large_converter_index(self): + write_shard(self.model / "out-1000000000000.safetensors", [ + ("sparse.weight", 1), + ]) + + checks = self.checks_by_id(self.report(deep=True)) + sequence = checks["model.shard_sequence"] + + self.assertEqual(sequence["status"], "fail") + self.assertEqual(sequence["details"]["last_shard"], 1_000_000_000_000) + self.assertEqual(sequence["details"]["missing_shards"], 1_000_000_000_000) + + def test_deep_check_rejects_mixed_shard_filename_schemes(self): + write_shard(self.model / "model-00001-of-00001.safetensors", [ + ("hf.weight", 1), + ]) + write_shard(self.model / "out-00000.safetensors", [("out.weight", 1)]) + + checks = self.checks_by_id(self.report(deep=True)) + + self.assertEqual(checks["model.container"]["status"], "pass") + self.assertEqual(checks["model.shard_sequence"]["status"], "fail") + self.assertIn("mixes", checks["model.shard_sequence"]["summary"]) + def test_deep_check_rejects_missing_core_tensor(self): write_shard(self.model / "model.safetensors", [ ("model.embed_tokens.weight", 1), @@ -280,6 +316,25 @@ def test_deep_check_validates_model_index(self): self.assertEqual(checks["model.index"]["status"], "pass") self.assertEqual(checks["model.index"]["details"]["indexed_tensors"], len(tensors)) + def test_deep_check_reports_non_object_model_index(self): + (self.model / "model.safetensors.index.json").write_text("[]") + + checks = self.checks_by_id(self.report(deep=True)) + + self.assertEqual(checks["model.container"]["status"], "pass") + self.assertEqual(checks["model.index"]["status"], "fail") + self.assertIn("not a JSON object", checks["model.index"]["summary"]) + + def test_deep_check_bounds_model_index_read(self): + (self.model / "model.safetensors.index.json").write_text("{}") + + with mock.patch("doctor.MODEL_INDEX_MAX_BYTES", 1): + checks = self.checks_by_id(self.report(deep=True)) + + self.assertEqual(checks["model.container"]["status"], "pass") + self.assertEqual(checks["model.index"]["status"], "fail") + self.assertIn("exceeds 1 bytes", checks["model.index"]["summary"]) + def test_cli_deep_json_is_machine_readable(self): cli = Path(__file__).parents[1] / "coli" run = subprocess.run([ From 6764b6cf23dda1143bac0fcea082b8d8a20f246a Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:18:04 -0600 Subject: [PATCH 65/96] fix(colibri.c): conditional headers for Windows portability --- c/colibri.c | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/c/colibri.c b/c/colibri.c index fef41369..656fdc64 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -39,6 +39,12 @@ #endif #include /* fstat per mmap degli shard (COLI_MMAP) */ #include /* SIGINT = stop morbido del turno in serve mode */ +#elif defined(_WIN32) +#ifndef WIN32_LEAN_AND_MEAN +#define WIN32_LEAN_AND_MEAN +#endif +#include +#include /* Required for GetProcessMemoryInfo */ #endif #ifdef __linux__ #include /* statfs: real fs-type check for the 9p warning (below) */ From 961e81ff93d33415b5002389fdc76571ef4006d8 Mon Sep 17 00:00:00 2001 From: Egon Ruiter Date: Sat, 25 Jul 2026 21:23:36 +0200 Subject: [PATCH 66/96] perf(simd): add OpenMP SIMD reductions to hot-path matrix multiplication loops (#442) --- c/olmoe.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/c/olmoe.c b/c/olmoe.c index e770efe0..0673df18 100644 --- a/c/olmoe.c +++ b/c/olmoe.c @@ -134,6 +134,7 @@ static void matmul(float *y, const float *x, const float *W, int S, int I, int O for (int s = 0; s < S; s++) { const float *xs = x + (int64_t)s * I; float acc = 0.f; + #pragma omp simd reduction(+:acc) for (int i = 0; i < I; i++) acc += xs[i] * w[i]; y[(int64_t)s * O + o] = acc; } @@ -207,6 +208,7 @@ static void matmul_q(float *y, const float *x, const int8_t *q, const float *sca for (int o = 0; o < O; o++) { const int8_t *w = q + (int64_t)o * I; float acc = 0.f; + #pragma omp simd reduction(+:acc) for (int i = 0; i < I; i++) acc += x[i] * (float)w[i]; y[o] = acc * scale[o]; } From d8abaae6809ccfcdd9a3148f400c8bde833d6d74 Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:27:09 -0600 Subject: [PATCH 67/96] fix(colibri.c): add Windows fallbacks for now_s and rss_gb --- c/colibri.c | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 656fdc64..5245410b 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -349,13 +349,30 @@ static int parse_cuda_devices(const char *list, int *out){ return n; } #endif -static double now_s(void){ struct timespec t; clock_gettime(CLOCK_MONOTONIC,&t); return t.tv_sec+t.tv_nsec*1e-9; } -static double rss_gb(void){ struct rusage r; getrusage(RUSAGE_SELF,&r); +static double now_s(void){ +#ifdef _WIN32 + LARGE_INTEGER freq, count; + QueryPerformanceFrequency(&freq); + QueryPerformanceCounter(&count); + return (double)count.QuadPart / (double)freq.QuadPart; +#else + struct timespec t; clock_gettime(CLOCK_MONOTONIC,&t); return t.tv_sec+t.tv_nsec*1e-9; +#endif +} +static double rss_gb(void){ +#ifdef _WIN32 + PROCESS_MEMORY_COUNTERS_EX pmc = {0}; pmc.cb = sizeof(pmc); + if(GetProcessMemoryInfo(GetCurrentProcess(), (PROCESS_MEMORY_COUNTERS*)&pmc, sizeof(pmc))) + return pmc.WorkingSetSize / (1024.0 * 1024.0 * 1024.0); + return 0; +#else + struct rusage r; getrusage(RUSAGE_SELF,&r); #ifdef __APPLE__ return r.ru_maxrss/(1024.0*1024.0*1024.0); /* macOS: ru_maxrss in BYTE */ #else return r.ru_maxrss/(1024.0*1024.0); /* Linux: in KB */ #endif +#endif } /* ---- PROF=1: opt-in performance profile ---------------------------------- * Records per-forward decode latency and expert-file bytes fetched, then From da1c7654a5e2e922cf6ce7f9f52f702aed44da1d Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:29:58 -0600 Subject: [PATCH 68/96] fix(st.h): protect realloc with temporary variable --- c/st.h | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/c/st.h b/c/st.h index ab259ae2..4270053b 100644 --- a/c/st.h +++ b/c/st.h @@ -331,7 +331,12 @@ static void st_init_multi(shards *S, const char *snap_dir, const char *extra_dir if (bad_shape) { fprintf(stderr, "%s: tensor '%s' shape overflows int64 — refusing (hostile or corrupt file)\n", files[fi], name); exit(1); } - if (S->n == S->cap) { S->cap *= 2; S->t = realloc(S->t, S->cap*sizeof(st_tensor)); } + if (S->n == S->cap) { + S->cap *= 2; + st_tensor *nt = (st_tensor*)realloc(S->t, S->cap * sizeof(st_tensor)); + if (!nt) { fprintf(stderr, "OOM reallocating shard tensor array\n"); exit(1); } + S->t = nt; + } st_tensor *t = &S->t[S->n++]; t->name = strdup(name); t->fd = fd; t->off = data_start + a0; t->nbytes = b0 - a0; t->dtype = st_dtype_code(dt->str); t->numel = numel; From 7445150b6796e526117a4f6818e1457f0f4fa7d3 Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:39:01 -0600 Subject: [PATCH 69/96] fix(st.h): replace strcpy with snprintf in shard sort buffer --- c/st.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c/st.h b/c/st.h index 4270053b..33108efb 100644 --- a/c/st.h +++ b/c/st.h @@ -271,7 +271,7 @@ static void st_init_multi(shards *S, const char *snap_dir, const char *extra_dir ndir, nf, snap_dir, c0); } for (int a = 0; a < nf; a++) for (int b = a+1; b < nf; b++) - if (strcmp(files[a], files[b]) > 0) { char tmp[1024]; strcpy(tmp, files[a]); strcpy(files[a], files[b]); strcpy(files[b], tmp); } + if (strcmp(files[a], files[b]) > 0) { char tmp[1024]; snprintf(tmp, sizeof(tmp), "%s", files[a]); snprintf(files[a], 1024, "%s", files[b]); snprintf(files[b], 1024, "%s", tmp); } for (int fi = 0; fi < nf; fi++) { int fd = st_open_fd(S, files[fi]); From 994c061d03c9a259f9ac18b69f76b332bb80c307 Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:41:01 -0600 Subject: [PATCH 70/96] fix(st.h): validate negative numel in st_read_f32_cap --- c/st.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c/st.h b/c/st.h index 33108efb..0584f11f 100644 --- a/c/st.h +++ b/c/st.h @@ -438,7 +438,7 @@ static int64_t st_read_f32(shards *S, const char *name, float *out, int drop) { static int64_t st_read_f32_cap(shards *S, const char *name, float *out, int64_t cap, int drop) { st_tensor *t = st_find(S, name); if (!t) { fprintf(stderr, "missing tensor: %s\n", name); exit(1); } - if (t->numel > cap) { + if (t->numel < 0 || t->numel > cap) { fprintf(stderr, "tensor %s: numel %lld exceeds destination capacity %lld\n", name, (long long)t->numel, (long long)cap); exit(1); } return st_read_f32(S, name, out, drop); From c1869cc00e48c1ac0f3576a3d06ff2187af29557 Mon Sep 17 00:00:00 2001 From: 4ny3l Date: Sat, 25 Jul 2026 13:42:53 -0600 Subject: [PATCH 71/96] fix(st.h): prevent integer overflow in st_read_slice_f32 bounds check --- c/st.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c/st.h b/c/st.h index 0584f11f..3221fcde 100644 --- a/c/st.h +++ b/c/st.h @@ -467,7 +467,7 @@ static void st_read_slice_f32(shards *S, const char *name, int64_t elem_off, int st_tensor *t = st_find(S, name); if (!t) { fprintf(stderr, "missing tensor: %s\n", name); exit(1); } int esz = (t->dtype == 2) ? 4 : 2; - if (elem_off < 0 || n_elems < 0 || elem_off + n_elems > t->numel) { /* keep the slice inside the tensor */ + if (elem_off < 0 || n_elems < 0 || elem_off > t->numel || n_elems > t->numel - elem_off) { /* keep the slice inside the tensor; subtraction avoids overflow (#1) */ fprintf(stderr, "slice %s [%lld,+%lld) out of tensor bounds (numel %lld)\n", name, (long long)elem_off, (long long)n_elems, (long long)t->numel); exit(1); } int64_t boff = t->off + elem_off * esz, nb = n_elems * esz; From 751e141aba2299f84c53d55e685b81eb0135494c Mon Sep 17 00:00:00 2001 From: manjumihir Date: Sun, 26 Jul 2026 07:06:19 +0700 Subject: [PATCH 72/96] telemetry: read real hwinfo on macOS (sysctl + host_statistics64) hw_probe() read /proc/cpuinfo and /proc/meminfo on every non-Windows platform, so on macOS the dashboard hardware panel and /health hwinfo reported an empty CPU name and 0 GB RAM total/available. Add a __APPLE__ path: machdep.cpu.brand_string for the CPU name, hw.memsize for total RAM, and host_statistics64 free+inactive+purgeable pages (the macOS analogue of Linux MemAvailable) for available RAM. No new dependencies; Linux and Windows paths unchanged. Verified on an M2 Max (macOS 26 / Darwin 25.5): clean METAL=1 build with -Wall -Wextra and zero warnings, make test-c green, probe reports cpu='Apple M2 Max' ram_total=103.1 ram_avail=42.6. Co-Authored-By: Claude Fable 5 --- c/telemetry.h | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/c/telemetry.h b/c/telemetry.h index 3c3fe855..074e1625 100644 --- a/c/telemetry.h +++ b/c/telemetry.h @@ -37,6 +37,10 @@ static void ehit_mark(Model *m, int layer, int eid){ } /* CPU model + cores + RAM (GB); empty/zero where unavailable. */ +#ifdef __APPLE__ +#include +#include +#endif static void hw_probe(char *cpu, size_t cn, int *cores, double *ram_total, double *ram_avail){ cpu[0]=0; #ifdef _WIN32 @@ -47,6 +51,8 @@ static void hw_probe(char *cpu, size_t cn, int *cores, double *ram_total, double char *b=(char*)r; b[47]=0; while(*b==' ')b++; snprintf(cpu,cn,"%s",b); } #endif +#elif defined(__APPLE__) + { size_t sl=cn; if(sysctlbyname("machdep.cpu.brand_string",cpu,&sl,NULL,0)) cpu[0]=0; } #else FILE *ci=fopen("/proc/cpuinfo","r"); if(ci){ char ln[256]; @@ -65,6 +71,15 @@ static void hw_probe(char *cpu, size_t cn, int *cores, double *ram_total, double *ram_total=*ram_avail=0; #ifdef _WIN32 compat_meminfo(ram_total,ram_avail); +#elif defined(__APPLE__) + { uint64_t ms=0; size_t sl=sizeof(ms); + if(!sysctlbyname("hw.memsize",&ms,&sl,NULL,0)) *ram_total=(double)ms/1e9; + int64_t pgsz=0; sl=sizeof(pgsz); + if(sysctlbyname("hw.pagesize",&pgsz,&sl,NULL,0)||pgsz<=0) pgsz=16384; + vm_statistics64_data_t vs; mach_msg_type_number_t nc=HOST_VM_INFO64_COUNT; + if(host_statistics64(mach_host_self(),HOST_VM_INFO64,(host_info64_t)&vs,&nc)==KERN_SUCCESS) + /* macOS analogue of Linux MemAvailable: free + inactive + purgeable */ + *ram_avail=(double)(vs.free_count+vs.inactive_count+vs.purgeable_count)*(double)pgsz/1e9; } #else FILE *mi=fopen("/proc/meminfo","r"); if(mi){ char ln[256]; double mt=0,ma=0; From a9b8a476d060ba87cf7f4ec42221be15ce7093ab Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sun, 26 Jul 2026 03:06:06 +0200 Subject: [PATCH 73/96] metal/oracle: scope the token-exact claim to decode + implement DEBUG_LOGITS top-5 dump (#622) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit @lBroth reported that Metal prefill GEMM (matmul_qt on GPU, S >= COLI_METAL_GEMM_MIN, default 16) is not token-exact vs CPU on NEAR-TIE logits: on 4-bit toy fixtures a teacher-forced prefill disagrees at 2/32 positions, deterministically. Decode is unaffected (S=1, and short prompts stay below the GEMM threshold). The GEMM kernel is numerically fine (make metal-test passes at ~3e-6 vs a 1e-4 tolerance); it is a GPU-vs-CPU accumulation-order difference that only flips the argmax when two logits sit inside that drift. Confirmed: COLI_METAL_GEMM_MIN=100000 (keep every GEMM on the CPU) makes it bit-identical. So the concrete defect is a docs overclaim, not a kernel bug. - docs/metal.md: "Token-exact vs the CPU path" -> decode is token-exact; prefill's GPU GEMM can diverge on near-tie logits by accumulation order (not a kernel bug, #622), with COLI_METAL_GEMM_MIN=100000 as the bit-exact escape hatch for teacher-forced comparisons. - Implement DEBUG_LOGITS: the oracle mismatch message pointed at "TF=1 DEBUG_LOGITS=1 for top-5 logit dump", but no such flag existed (a dead string, also flagged in #622). It now works: on a teacher-forcing mismatch it dumps the top-5 logits, the top1-top2 margin, and the expected/got tokens' logits, so a near-tie divergence reads as the tiny gap it is instead of a bare token mismatch. Opt-in, stderr, fires only on a mismatch — normal runs are byte-for-byte unchanged (forward_all gains a nullable ref arg; its one caller updated). Verified: oracle stays 32/32 with DEBUG_LOGITS=1 on a clean run (no dump); a forced mismatch prints e.g. "[LOGITS] pos=5 top1-top2 gap=2.449e-03 | expected=35 (-0.23799) got=34 (1.73265) | top5: 34:1.73265 197:1.73020 ...". Also relevant to #457/#587: verify Metal fmt=4 token-exactness with COLI_METAL_GEMM_MIN=100000 to isolate the new kernel from this pre-existing prefill drift. Co-Authored-By: Claude Opus 4.8 (1M context) --- c/colibri.c | 24 ++++++++++++++++++++++-- docs/metal.md | 10 +++++++++- 2 files changed, 31 insertions(+), 3 deletions(-) diff --git a/c/colibri.c b/c/colibri.c index 3b0f1371..869f9cb3 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -4805,8 +4805,27 @@ static void emit_stream(int t, void *ud){ } /* teacher-forcing: un solo forward su ids[S], argmax per posizione in pred[S] */ -static void forward_all(Model *m, const int *ids, int S, int *pred){ +/* DEBUG_LOGITS=1: on a teacher-forcing mismatch, dump the top-5 logits, the top1-top2 margin, + * and where the expected/got tokens land. Makes a near-tie divergence (e.g. the Metal prefill + * GEMM accumulation-order drift, #622) visible as the tiny gap it is, rather than a bare token + * mismatch. Stderr, opt-in, only on a mismatch — normal runs are byte-for-byte unchanged. */ +static void dump_top5_logits(int pos, const float *lo, int V, int expected, int got){ + int idx[5]; float val[5]; + for(int k=0;k<5;k++){ idx[k]=-1; val[k]=-INFINITY; } + for(int i=0;ival[k]){ + for(int j=4;j>k;j--){ val[j]=val[j-1]; idx[j]=idx[j-1]; } + val[k]=v; idx[k]=i; break; } } + double gap = (idx[1]>=0) ? (double)(val[0]-val[1]) : 0.0; + fprintf(stderr,"[LOGITS] pos=%d top1-top2 gap=%.3e | expected=%d (%.5f) got=%d (%.5f) | top5:", + pos, gap, expected, (expected>=0&&expected=0&&got=0;k++) fprintf(stderr," %d:%.5f", idx[k], (double)val[k]); + fprintf(stderr,"\n"); +} +static void forward_all(Model *m, const int *ids, int S, int *pred, const int *ref){ Cfg *c=&m->c; int D=c->hidden; + int dbg = ref && getenv("DEBUG_LOGITS"); kv_alloc(m,S); float *x=falloc((int64_t)S*D); for(int s=0;slm_head, 1); int best=0; float bv=lo[0]; for(int i=1;ivocab;i++) if(lo[i]>bv){bv=lo[i];best=i;} pred[s]=best; + if(dbg && pred[s]!=ref[s]) dump_top5_logits(s, lo, c->vocab, ref[s], pred[s]); } free(x); free(lo); free(row); } @@ -6998,7 +7018,7 @@ int main(int argc, char **argv){ if(getenv("TF")){ int *tf=read_arr(ref,"tf_pred",&(int){0}); int *pred=malloc(nfull*sizeof(int)); double tt=now_s(); - forward_all(&m, full, nfull, pred); double tdt=now_s()-tt; + forward_all(&m, full, nfull, pred, tf); double tdt=now_s()-tt; int ok=0; for(int i=0;i Date: Sun, 26 Jul 2026 08:57:11 +0530 Subject: [PATCH 74/96] Apply the implicit GLM stop sequences on /v1/messages too The Anthropic endpoint never went through StopFilter. `generation_options`'s stop sequences were discarded into `_stop_sequences`, and both the streaming and non-streaming Anthropic paths called `engine.generate` with a raw sink, so the stop policy that /v1/chat/completions gained never reached /v1/messages. In serve mode the engine arms only <|endoftext|> and filters the other stop ids (#401, and #549 for the containers where another end-of-turn special wins the final-token margin). Role markers are the gateway's boundary to enforce -- so on /v1/messages the model's <|user|> was detokenized into the reply and generation ran on past the end of the turn until it hit max_tokens. Measured on a GLM-5.2 int4 container (DGX Spark GB10, coli serve): "Reply with the single word: ready" -> ready<|user|><|user|>The user is asking for a recipe for a cake. What is Claude Code is the reference client from #343 and speaks only /v1/messages, so it saw this on every reply while OpenAI-shaped clients were already clean. Reuse stop_policy()/StopFilter rather than adding a second mechanism: `body` here is the translated OpenAI-shaped request and anthropic_messages() has already refused a client `stop_sequences`, so it resolves to the same implicit GLM role boundaries. Passing stop_filter.stopped also ends generation at the marker instead of decoding a tail that is thrown away -- worth real time on a streaming 744B model (a 200-token budget returned in 34s instead of ~4min). Tests fail without the fix and cover both response shapes plus a marker split across engine chunks. make check: 213 passed. Co-authored-by: Cursor --- c/openai_server.py | 16 +++++++++---- c/tests/test_anthropic_messages.py | 37 +++++++++++++++++++++++++++++- 2 files changed, 48 insertions(+), 5 deletions(-) diff --git a/c/openai_server.py b/c/openai_server.py index 496de3c0..cf2f5279 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -1989,6 +1989,10 @@ def anthropic_messages(self, body, request_id): def anthropic_generation(self, body, prompt, request_id, tools, enable_thinking): maximum, temperature, top_p, grammar, _stop_sequences = generation_options( body, self.server.max_tokens) + # Same policy as /v1/chat/completions: `body` is the translated OpenAI-shaped + # request, and anthropic_messages() has already refused a client `stop_sequences`, + # so this resolves to the implicit GLM role boundaries. + stop_sequences, ignore_leading_stop = stop_policy(body, True) cache_slot = body.get("cache_slot") if (cache_slot is not None and (isinstance(cache_slot, bool) or not isinstance(cache_slot, int) or @@ -2028,9 +2032,11 @@ def blocks_and_stop(text, stats): queue_headers = {"x-colibri-queue-wait-ms": str(round(queue_wait * 1000))} if not stream: output = [] + stop_filter = StopFilter(stop_sequences, output.append, ignore_leading_stop) stats = self.server.engine.generate( - prompt, maximum, temperature, top_p, output.append, cache_slot, - self.client_disconnected, grammar=grammar) + prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, + self.client_disconnected, grammar=grammar, stopped=stop_filter.stopped) + stop_filter.finish() content, stop_reason = blocks_and_stop("".join(output), stats) self.send_json(200, { "id": message_id, "type": "message", "role": "assistant", @@ -2147,9 +2153,11 @@ def on_text(chunk): raw.append(chunk) (split.feed if split else emit_answer)(chunk) + stop_filter = StopFilter(stop_sequences, on_text, ignore_leading_stop) stats = self.server.engine.generate( - prompt, maximum, temperature, top_p, on_text, cache_slot, - lambda: not connected[0], grammar=grammar) + prompt, maximum, temperature, top_p, stop_filter.feed, cache_slot, + lambda: not connected[0], grammar=grammar, stopped=stop_filter.stopped) + stop_filter.finish() if split: split.finish() close_thinking() # budget exhaustion before diff --git a/c/tests/test_anthropic_messages.py b/c/tests/test_anthropic_messages.py index b34c00a3..fcb7088f 100644 --- a/c/tests/test_anthropic_messages.py +++ b/c/tests/test_anthropic_messages.py @@ -28,10 +28,14 @@ def __init__(self, script=("Hé", "llo"), length_limited=False): self.prompts = [] def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0, - cancelled=None, grammar=None): + cancelled=None, grammar=None, stopped=None, on_accept=None): self.prompts.append(prompt) + self.emitted = 0 for chunk in self.script: on_text(chunk) + self.emitted += 1 + if stopped is not None and stopped(): + break # the gateway hit a stop sequence return {"prompt_tokens": 11, "completion_tokens": 3, "length_limited": self.length_limited} @@ -182,6 +186,37 @@ def test_stream_emits_named_events_in_order(self): self.assertEqual(payloads[-2]["delta"]["stop_reason"], "end_turn") self.assertEqual(payloads[-2]["usage"]["output_tokens"], 3) + def test_role_marker_ends_the_turn(self): + """/v1/messages must apply the same implicit GLM stop sequences as + /v1/chat/completions. In serve mode the engine arms only <|endoftext|> + (#401/#549), so <|user|> arrives as ordinary text; without the filter it is + detokenized into the reply and generation runs on to max_tokens.""" + script = ("ready", "<|user|>", "the user asks about cake") + self.engine.script = script + with self.post(self.base_body()) as response: + payload = json.load(response) + self.assertEqual(payload["content"], [{"type": "text", "text": "ready"}]) + self.assertEqual(payload["stop_reason"], "end_turn") + self.assertEqual(self.engine.emitted, 2) # tail cancelled, not merely hidden + + def test_role_marker_ends_a_streamed_turn(self): + self.engine.script = ("ready", "<|user|>", "the user asks about cake") + with self.post(self.base_body(stream=True)) as response: + raw = response.read().decode() + self.assertNotIn("<|user|>", raw) + deltas = [json.loads(line[len("data: "):]) for line in raw.splitlines() + if line.startswith("data: ")] + text = "".join(d["delta"]["text"] for d in deltas + if d["type"] == "content_block_delta" and "text" in d["delta"]) + self.assertEqual(text, "ready") + self.assertEqual(self.engine.emitted, 2) + + def test_marker_split_across_chunks_is_still_caught(self): + self.engine.script = ("read", "y<|", "user|>", "cake") + with self.post(self.base_body()) as response: + payload = json.load(response) + self.assertEqual(payload["content"], [{"type": "text", "text": "ready"}]) + def test_tool_call_becomes_tool_use_block(self): self.engine.script = ("Sure. get_weathercity" "Rome",) From ce25594bde7bf0b288334c2b99e308fd94f3a97a Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Sun, 26 Jul 2026 12:48:11 +0800 Subject: [PATCH 75/96] cuda: fmt=6 (E8/IQ3) decode, so E8 containers can reach VRAM fmt=6 had no GPU kernel, so qt_cuda_upload refused it outright and every E8 tensor stayed CPU-side. That inverts the format's whole purpose: E8 buys -25% expert bytes to fit MORE experts in VRAM (#452), but as shipped it pushed all of them onto the CPU tier -- the ~57 ms/token wall measured in #431. Decode is per 32-weight sub-block (a 98-byte super-block holds 64 codebook indices, 8 words of 4x7 signs + 4-bit sub-scale, and one fp16 scale), so threads stride over sub-blocks rather than elements and expand once per 32 weights. Nothing inside a 98-byte block is guaranteed aligned, hence the byte-wise word assembly; the fp16 conversion mirrors e8_fp16_to_f32 rather than calling __half2float so the two decoders cannot drift on subnormals. The activation rotation splits exactly as it does on the CPU path: all routed experts of a layer share the gate/up input, so the CALLER rotates it once per layer (~1.4 ms vs ~11 ms per-expert on GLM dims), while the down projection's input is the per-expert silu product and must be rotated on the device -- e8_rot_rows_kernel, staging each power-of-two block in shared memory and regenerating the sign stream in-kernel from the same xorshift64* as quant.h, so no rotation data is stored or uploaded. moe() fed x straight to the GPU at three packing sites (the overlap issue path, the sync group path, and the group-failure fallback); all three now take the rotated copy through one E8_XE helper. The fallback's CPU recompute was also missing the down-input rotation the main CPU path does. Two symmetry fixes the format needs, both previously unreachable because fmt=6 could not be uploaded at all: the tensor accounting in coli_cuda_tensor_free charged for a scale buffer fmt=6 never allocates, which tripped the >= guard and leaked the byte count permanently; and coli_cuda_tensor_update would memcpy O floats out of a NULL scales pointer on a repin. The codebook is published from quant.h to each device rather than copied into the backend, so there is one source of truth. The Windows loader resolves the entry point optionally -- an older DLL degrades fmt=6 to the CPU instead of disabling the whole GPU backend. Tests: fmt=6 matmul and the full expert MLP (including the device-side rotation) against a decoder written from the format description, not shared with quant.h, so a common-mode error cannot hide. The synthetic codebook also exercises the upload path the real table travels. make check 211; cuda-test green on 1 and 2 devices (RTX 5090, sm_120). Refs #452, #431 --- c/backend_cuda.cu | 155 ++++++++++++++++++++++++++-- c/backend_cuda.h | 5 + c/backend_gpu_compat.h | 1 + c/backend_loader.c | 17 +++ c/colibri.c | 31 ++++-- c/tests/test_backend_cuda.cu | 194 ++++++++++++++++++++++++++++++++++- 6 files changed, 385 insertions(+), 18 deletions(-) diff --git a/c/backend_cuda.cu b/c/backend_cuda.cu index 71355c25..5a739cd9 100644 --- a/c/backend_cuda.cu +++ b/c/backend_cuda.cu @@ -87,15 +87,61 @@ static int select_ctx(DeviceContext *ctx) { return 1; } +/* fmt=6 (E8/IQ3) geometry, mirroring quant.h. A super-block packs 256 weights + * into 98 bytes: 64 codebook indices, 8 words of (4x7 signs + 4-bit sub-scale), + * and one fp16 super-scale. Scales live INSIDE the block, so fmt=6 tensors carry + * no separate scale array (#452). */ +#define COLI_E8_QK 256 +#define COLI_E8_SUB 32 +#define COLI_E8_BBYTES 98 + __host__ __device__ static size_t row_bytes(int fmt, int I) { if (fmt == 0) return (size_t)I * sizeof(float); if (fmt == 1) return (size_t)I; if (fmt == 2 || fmt == 4) return (size_t)(I + 1) / 2; /* fmt=4: same packed int4 */ if (fmt == 3) return (size_t)(I + 3) / 4; if (fmt == 4) return (size_t)(I + 1) / 2; /* grouped int4: nibbles like fmt 2 */ + if (fmt == 6) return (size_t)(((int64_t)I + COLI_E8_QK - 1) / COLI_E8_QK) * COLI_E8_BBYTES; return 0; } +/* The E8 codebook, uploaded once per device from quant.h's e8_grid so the table + * has a single source of truth and cannot drift from the CPU decoder. */ +__constant__ uint8_t c_e8_grid[256][4]; + +/* A super-block is 98 bytes, so nothing inside it is guaranteed 4- or 2-byte + * aligned: assemble the words byte-wise instead of dereferencing. */ +__device__ __forceinline__ uint32_t e8_ld_u32(const uint8_t *p){ + return (uint32_t)p[0] | ((uint32_t)p[1]<<8) | ((uint32_t)p[2]<<16) | ((uint32_t)p[3]<<24); +} +/* Mirrors e8_fp16_to_f32 rather than calling __half2float, so the two decoders + * cannot disagree on subnormals. */ +__device__ __forceinline__ float e8_fp16(const uint8_t *p){ + uint16_t h = (uint16_t)p[0] | ((uint16_t)p[1]<<8); + uint32_t sign=(uint32_t)(h&0x8000)<<16, exp=(h>>10)&0x1F, man=h&0x3FF, bits; + if (!exp) bits = man ? (sign|((127u-15u+1u-1u)<<23)|(man<<13)) : sign; + else if (exp==31) bits = sign|0x7F800000u|(man<<13); + else bits = sign|((exp+112u)<<23)|(man<<13); + float f; memcpy(&f,&bits,4); return f; +} +/* Expand one 32-weight sub-block; mirrors e8_expand_sub in quant.h. */ +__device__ __forceinline__ void e8_expand_sub_dev(const uint8_t *blk, int ib, float d, float *out){ + uint32_t word = e8_ld_u32(blk + COLI_E8_QK/4 + ib*4); + float db = d * (0.5f + (float)((word>>28)&0xF)) * 0.5f; + const uint8_t *idx = blk + ib*8; + for (int l=0;l<4;l++){ + uint32_t seven=(word>>(7*l))&0x7F; + const uint8_t *g0=c_e8_grid[idx[l*2+0]], *g1=c_e8_grid[idx[l*2+1]]; + int par=0; + for (int j=0;j<8;j++){ + int neg = j<7 ? (int)((seven>>j)&1) : 0; + if (j<7) par^=neg; else neg=par; /* odd parity closes the block */ + float mag = (j<4 ? (float)g0[j] : (float)g1[j-4]) * 0.5f; + out[l*8+j] = neg ? -mag*db : mag*db; + } + } +} + __device__ static float weight_at(const void *weights, int fmt, size_t row, int i) { const uint8_t *base = static_cast(weights) + row; if (fmt == 0) return reinterpret_cast(base)[i]; @@ -133,7 +179,20 @@ __global__ static void quant_matmul(float *y, const float *x, const void *weight float sum = 0.0f; size_t row = (size_t)o * rb; const float *xs = x + (size_t)s * I; - if (fmt == 4) { + if (fmt == 6) { + /* E8/IQ3: decode is per 32-weight sub-block, so threads stride over + * sub-blocks rather than elements -- expanding once per 32 weights + * instead of redoing the word/parity work for every element. */ + const uint8_t *wrow = static_cast(weights) + row; + int nsub = (I + COLI_E8_SUB - 1) / COLI_E8_SUB; + for (int sb = threadIdx.x; sb < nsub; sb += blockDim.x) { + const uint8_t *blk = wrow + (size_t)(sb / (COLI_E8_QK/COLI_E8_SUB)) * COLI_E8_BBYTES; + float w[COLI_E8_SUB]; + e8_expand_sub_dev(blk, sb % (COLI_E8_QK/COLI_E8_SUB), e8_fp16(blk+96), w); + int off = sb*COLI_E8_SUB, n = I-off < COLI_E8_SUB ? I-off : COLI_E8_SUB; + for (int k=0;k 2048+4096, 1536 -> + * 512+1024). The sign stream is regenerated in-kernel from the same xorshift64* + * that quant.h's e8_signs uses, so no rotation data is stored or uploaded. + * + * Placement note: all routed experts of a layer share one gate/up input, so that + * rotation belongs to the CALLER (once per layer). This kernel exists for the + * down projection, whose input is the per-expert silu(gate)*up product and so + * cannot be shared -- mirroring colibri.c's split at moe(). */ +__global__ static void e8_rot_rows_kernel(float *rows, int dim, int off, int n){ + extern __shared__ float sh[]; + __shared__ uint8_t sbits[4096/8]; + if (!threadIdx.x) { + uint64_t s = 417u + (uint64_t)n; + for (int i=0;i<(n+7)/8;i++){ + s^=s>>12; s^=s<<25; s^=s>>27; + sbits[i] = (uint8_t)((s*2685821657736338717ULL)>>56); + } + } + __syncthreads(); + float *row = rows + (size_t)blockIdx.x*dim + off; + for (int i=threadIdx.x;i>3]>>(i&7)&1) ? -v : v; + } + __syncthreads(); + for (int len=1;len 4096) b >>= 1; + e8_rot_rows_kernel<<<(unsigned)nr, 256, (size_t)b*sizeof(float), stream>>>(rows, dim, off, b); + if (cudaGetLastError() != cudaSuccess) return 0; + off += b; + } + return 1; } __global__ static void silu_mul(float *gate, const float *up, size_t n) { @@ -495,6 +607,20 @@ static int reserve_pinned(float **ptr,size_t *cap,size_t bytes){ if(!cuda_ok(cudaMallocHost(ptr,bytes),"pinned staging allocation"))return 0;*cap=bytes;return 1; } +/* Publish quant.h's E8 codebook to every configured device. __constant__ memory + * is per-device, so this walks the contexts; the engine calls it once after init + * rather than the backend carrying a second copy of the table that could drift + * from the CPU decoder's (#452). Safe to call before any fmt=6 upload only. */ +extern "C" int coli_cuda_e8_set_grid(const void *grid) { + if (!grid || g_nctx < 1) return 0; + for (int i = 0; i < g_nctx; i++) { + if (!select_ctx(&g_ctx[i])) return 0; + if (!cuda_ok(cudaMemcpyToSymbol(c_e8_grid, grid, sizeof(c_e8_grid)), "E8 codebook upload")) + return 0; + } + return 1; +} + extern "C" int coli_cuda_init(const int *devices, int count) { #if defined(__HIP_PLATFORM_AMD__) || defined(__HIP__) /* #509: the ROCm runtime (comgr, MIOpen, roctracer) reads $TEMP as a temp-dir @@ -625,7 +751,9 @@ extern "C" int coli_cuda_tensor_upload(ColiCudaTensor **tensor, DeviceContext *ctx = find_ctx(device); if (!weights || I < 1 || O < 1 || !select_ctx(ctx)) return 0; size_t rb = row_bytes(fmt, I); - if (!rb || (fmt && !scales)) return 0; + /* fmt=6 keeps its scales inside each 98-byte block, so it is the one + * quantized format that legitimately arrives with scales == NULL. */ + if (!rb || (fmt && fmt != 6 && !scales)) return 0; ColiCudaTensor *t = static_cast(std::calloc(1, sizeof(*t))); if (!t) return 0; t->fmt = fmt; t->I = I; t->O = O; t->device = device; t->weight_bytes = rb * (size_t)O; @@ -640,16 +768,17 @@ extern "C" int coli_cuda_tensor_upload(ColiCudaTensor **tensor, if(fmt==2||fmt==4){ /* same nibble layout: offset-binary -> signed in place */ offset_to_signed_s4<<<(unsigned)((t->weight_bytes+255)/256),256>>>((uint8_t*)t->weights,t->weight_bytes); if(!cuda_ok(cudaGetLastError(),"int4 weight conversion")){coli_cuda_tensor_free(t);return 0;}} - if (fmt) { + if (fmt && fmt != 6) { if (!cuda_ok(cudaMalloc(&t->scales, t->scale_count * sizeof(float)), "scale allocation") || !cuda_ok(cudaMemcpy(t->scales, scales, t->scale_count * sizeof(float), cudaMemcpyHostToDevice), "scale upload")) { coli_cuda_tensor_free(t); return 0; } } + if (fmt == 6) t->scale_count = 0; /* in-block scales: nothing separate to track */ t->tracked = 1; ctx->tensor_count++; - ctx->tensor_bytes += t->weight_bytes + (fmt ? t->scale_count * sizeof(float) : 0); + ctx->tensor_bytes += t->weight_bytes + ((fmt && fmt != 6) ? t->scale_count * sizeof(float) : 0); *tensor = t; return 1; } @@ -665,7 +794,7 @@ extern "C" int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor, extern "C" int coli_cuda_tensor_update(ColiCudaTensor *tensor, const void *weights, const float *scales) { - if (!tensor || !weights || (tensor->fmt && !scales)) return 0; + if (!tensor || !weights || (tensor->fmt && tensor->fmt != 6 && !scales)) return 0; DeviceContext *ctx=find_ctx(tensor->device); if (!select_ctx(ctx)) return 0; if (!cuda_ok(cudaMemcpy(tensor->weights,weights,tensor->weight_bytes, @@ -676,7 +805,9 @@ extern "C" int coli_cuda_tensor_update(ColiCudaTensor *tensor, if(!cuda_ok(cudaGetLastError(),"int4 weight refresh")) return 0; } int ng = tensor->ng > 0 ? tensor->ng : 1; - return !tensor->fmt || cuda_ok(cudaMemcpy(tensor->scales,scales, + /* fmt=6 has no scale buffer at all (scales live in-block, scale_count 0), and + * the fallback below would otherwise copy O floats out of a NULL host pointer. */ + return !tensor->fmt || tensor->fmt==6 || cuda_ok(cudaMemcpy(tensor->scales,scales, (tensor->scale_count?tensor->scale_count:(size_t)tensor->O)*sizeof(float), cudaMemcpyHostToDevice),"scale refresh"); } @@ -746,6 +877,10 @@ extern "C" int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up, up->fmt,S,D,I,row_bytes(up->fmt,D),up->gs,up->ng); size_t n=(size_t)S*I; silu_mul<<<(unsigned)((n+255)/256),256>>>(ctx->gate,ctx->up,n); + /* fmt=6: the down projection stores W@Q, so its input needs Q^T applied. This + * one is per-expert (the silu product is not shared), unlike the gate/up input + * rotation, which the caller does once per layer -- same split as moe(). */ + if (down->fmt == 6 && !e8_rot_rows_dev(ctx->gate, S, I, 0)) return 0; quant_matmul<<>>(ctx->y,ctx->gate,down->weights,down->scales, down->fmt,S,I,D,row_bytes(down->fmt,I),down->gs,down->ng); if (!cuda_ok(cudaGetLastError(),"expert MLP launch") || @@ -1159,7 +1294,11 @@ extern "C" void coli_cuda_tensor_free(ColiCudaTensor *tensor) { if (ctx) select_ctx(ctx); if (tensor->tracked && ctx) { int ng = tensor->ng > 0 ? tensor->ng : 1; - size_t bytes = tensor->weight_bytes + (tensor->fmt ? (size_t)tensor->O * ng * sizeof(float) : 0); + /* Must mirror the upload's accounting exactly: fmt=6 never charged for a + * scale buffer, and over-subtracting here trips the >= guard below, which + * silently leaves the tensor's bytes on the device counter forever. */ + size_t bytes = tensor->weight_bytes + + ((tensor->fmt && tensor->fmt != 6) ? (size_t)tensor->O * ng * sizeof(float) : 0); if (ctx->tensor_count) ctx->tensor_count--; if (ctx->tensor_bytes >= bytes) ctx->tensor_bytes -= bytes; } diff --git a/c/backend_cuda.h b/c/backend_cuda.h index b83c58a9..e3198368 100644 --- a/c/backend_cuda.h +++ b/c/backend_cuda.h @@ -35,6 +35,11 @@ COLI_CUDA_DLLEXPORT void coli_cuda_stats(int device, size_t *tensor_count, size_ COLI_CUDA_DLLEXPORT void coli_cuda_group_stats(uint64_t *calls, uint64_t *experts, uint64_t *rows, double *h2d_ms, double *kernel_ms, double *d2h_ms); +/* Publish the E8 codebook (quant.h's e8_grid, 256x4 bytes) to every configured + * device. Must be called after coli_cuda_init and before any fmt=6 upload; the + * backend keeps no copy of the table so it cannot drift from the CPU decoder. */ +COLI_CUDA_DLLEXPORT int coli_cuda_e8_set_grid(const void *grid); + /* Upload without executing, so capacity failures happen during model startup. */ COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor, const void *weights, const float *scales, diff --git a/c/backend_gpu_compat.h b/c/backend_gpu_compat.h index 91b9f285..74bbd578 100644 --- a/c/backend_gpu_compat.h +++ b/c/backend_gpu_compat.h @@ -47,6 +47,7 @@ namespace nvcuda { namespace wmma = ::rocwmma; } #define cudaMemcpy hipMemcpy #define cudaMemcpy2D hipMemcpy2D #define cudaMemcpyAsync hipMemcpyAsync +#define cudaMemcpyToSymbol hipMemcpyToSymbol /* fmt=6 E8 codebook upload */ #define cudaMemcpyHostToDevice hipMemcpyHostToDevice #define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost #define cudaMemGetInfo hipMemGetInfo diff --git a/c/backend_loader.c b/c/backend_loader.c index 21be5484..abf0d99e 100644 --- a/c/backend_loader.c +++ b/c/backend_loader.c @@ -52,6 +52,7 @@ typedef int (*fn_attention_absorb)(ColiCudaTensor *kv_b, float *ctx, typedef int (*fn_tensor_upload)(ColiCudaTensor **tensor, const void *weights, const float *scales, int fmt, int I, int O, int device); typedef int (*fn_tensor_upload_g)(ColiCudaTensor **tensor, const void *weights, const float *scales, int fmt, int I, int O, int device, int gs); +typedef int (*fn_e8_set_grid)(const void *grid); typedef int (*fn_matmul)(ColiCudaTensor **tensor, float *y, const float *x, const void *weights, const float *scales, int fmt, int S, int I, int O, int device, int gs); @@ -114,6 +115,7 @@ static struct { fn_attention_absorb attention_absorb; fn_tensor_upload tensor_upload; fn_tensor_upload_g tensor_upload_g; + fn_e8_set_grid e8_set_grid; fn_matmul matmul; fn_tensor_free tensor_free; fn_tensor_bytes tensor_bytes; @@ -200,6 +202,15 @@ static int coli_cuda_load(void){ fprintf(stderr, "[CUDA] coli_cuda.dll missing symbol coli_cuda_" #name "\n"); \ FreeLibrary(g_cuda.dll); g_cuda.dll=NULL; return 0; } + /* Optional symbol: a DLL predating it leaves the pointer NULL and only that + * feature degrades (fmt=6 tensors stay CPU-side), instead of taking the whole + * GPU backend down over one missing entry point. */ + #define RESOLVE_OPT(name, type) \ + _Pragma("GCC diagnostic push") \ + _Pragma("GCC diagnostic ignored \"-Wcast-function-type\"") \ + g_cuda.name = (type)GetProcAddress(g_cuda.dll, "coli_cuda_" #name); \ + _Pragma("GCC diagnostic pop") + RESOLVE(init, fn_init) RESOLVE(shutdown, fn_shutdown) RESOLVE(device_count, fn_device_count) @@ -214,6 +225,7 @@ static int coli_cuda_load(void){ RESOLVE(attention_absorb, fn_attention_absorb) RESOLVE(tensor_upload, fn_tensor_upload) RESOLVE(tensor_upload_g, fn_tensor_upload_g) + RESOLVE_OPT(e8_set_grid, fn_e8_set_grid) RESOLVE(matmul, fn_matmul) RESOLVE(tensor_free, fn_tensor_free) RESOLVE(tensor_bytes, fn_tensor_bytes) @@ -341,6 +353,11 @@ int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor, const void *weights, cons return g_cuda.tensor_upload_g(tensor, weights, scales, fmt, I, O, device, gs); } +int coli_cuda_e8_set_grid(const void *grid){ + if(!g_cuda.available || !g_cuda.e8_set_grid) return 0; /* fmt=6 stays CPU-side */ + return g_cuda.e8_set_grid(grid); +} + int coli_cuda_matmul(ColiCudaTensor **tensor, float *y, const float *x, const void *weights, const float *scales, int fmt, int S, int I, int O, int device, int gs){ diff --git a/c/colibri.c b/c/colibri.c index 53f6e799..8a978767 100644 --- a/c/colibri.c +++ b/c/colibri.c @@ -296,8 +296,10 @@ static void qt_cuda_reset(QT *t){ if(t->cuda){ coli_cuda_tensor_free(t->cuda); t->cuda=NULL; } t->cuda_failed=0; } +static int g_cuda_e8_ready; /* codebook published to the devices (see cuda_boot) */ static int qt_cuda_upload(QT *t){ - if(t->fmt==5||t->fmt==6) return 0; /* int3-g64 / E8: no CUDA kernel yet — tensor stays CPU-side */ + if(t->fmt==5) return 0; /* int3-g64: no CUDA kernel yet — tensor stays CPU-side */ + if(t->fmt==6 && !g_cuda_e8_ready) return 0; /* E8 without its codebook would decode garbage */ const void *weights = t->fmt==0 ? (const void*)t->qf : t->fmt==1 ? (const void*)t->q8 : (const void*)t->q4; if(t->fmt==4) /* grouped int4 (#334): scales are [O, ceil(I/gs)] — the plain @@ -3157,6 +3159,12 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int float *xg=falloc((int64_t)S*D), *gg=falloc((int64_t)S*I), *uu=falloc((int64_t)S*I), *hh=falloc((int64_t)S*D); float *xe=NULL; /* fmt=6: x under the rotation Q^T, built once per call — all routed * experts of the layer share it (the placement rule in quant.h) */ + /* Materialise xe on first use. Every site that feeds a routed expert's gate/up + * input — CPU, the per-expert GPU call, and all three group packing paths — + * must go through here: doing the transform per expert instead of per layer + * costs ~11 ms against ~1.4 ms on GLM dims (#452). */ + #define E8_XE(e) ((e)->g.fmt==6 ? (xe ? xe : (xe=falloc((int64_t)S*D), \ + memcpy(xe,x,(size_t)S*D*sizeof(float)), e8_rot_rows(xe,S,D), xe)) : x) int *rows=xalloc((size_t)S*sizeof(int),"moe rows"); float *rw=xalloc((size_t)S*sizeof(float),"moe rw"); #ifdef COLI_CUDA /* PIPE Inc.1b: il batch-union del prefill passa dai gruppi GPU — prima di @@ -3339,8 +3347,9 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int int nc=pd_nc[di]++; ESlot *e=pg_e[q]; pd_g[di][nc]=e->g.cuda; pd_u[di][nc]=e->u.cuda; pd_d[di][nc]=e->d.cuda; pd_rows[di][nc]=pg_n[q]; pd_which[di][nc]=q; + const float *xsrc=E8_XE(e); /* fmt=6 feeds the rotated copy */ for(int r=0;rg.fmt==6){ - if(!xe){ xe=falloc((int64_t)S*D); memcpy(xe,x,(size_t)S*D*sizeof(float)); e8_rot_rows(xe,S,D); } - xsrc=xe; - } + const float *xsrc=E8_XE(e); for(int r=0;rg.cuda; dev_u[di][nc]=e->u.cuda; dev_d[di][nc]=e->d.cuda; dev_rows[di][nc]=group_n[q]; dev_which[di][nc]=q; + const float *xsrc=E8_XE(e); /* fmt=6 feeds the rotated copy */ for(int r=0;rg.cuda,e->u.cuda,e->d.cuda,hh,xg,nr)){ expert_host_ensure(m,layer,e); expert_gate_up(gg,uu,xg,&e->g,&e->u,nr); for(int64_t z=0;z<(int64_t)nr*I;z++) gg[z]=siluf(gg[z])*uu[z]; + if(e->d.fmt==6) e8_rot_rows(gg,nr,I); /* down input, as on the main CPU path */ matmul_qt(hh,gg,&e->d,nr); if(g_prof){m->cpu_expert_bytes+=qt_bytes(&e->g)+qt_bytes(&e->u)+qt_bytes(&e->d); m->cpu_expert_rows+=(uint64_t)nr;} @@ -3678,6 +3686,7 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int shared_done: free(logits_all); free(choice); free(idxs); free(ws); free(keff); free(uniq); free(xg); free(gg); free(uu); free(hh); free(rows); free(rw); free(xe); + #undef E8_XE #ifdef COLI_CUDA free(group_x);free(group_y); free(group_row); free(group_weight); @@ -6817,6 +6826,10 @@ int main(int argc, char **argv){ if(g_cuda_ndev<1){ fprintf(stderr,"invalid COLI_GPUS: use a list such as 0,1,2\n"); return 2; } g_cuda_enabled=coli_cuda_init(g_cuda_devices,g_cuda_ndev); if(!g_cuda_enabled){ fprintf(stderr,"[CUDA] requested backend is unavailable\n"); return 2; } + /* fmt=6 decodes against quant.h's codebook; publish it to every device so + * the backend never keeps a second copy that could drift (#452). An older + * DLL without the symbol leaves this 0 and fmt=6 tensors stay CPU-side. */ + g_cuda_e8_ready=coli_cuda_e8_set_grid(e8_grid); } g_cuda_dense=getenv("CUDA_DENSE")?atoi(getenv("CUDA_DENSE")):0; g_cuda_pipe=getenv("COLI_CUDA_PIPE")?atoi(getenv("COLI_CUDA_PIPE")):0; diff --git a/c/tests/test_backend_cuda.cu b/c/tests/test_backend_cuda.cu index 1a826b8f..0253cf60 100644 --- a/c/tests/test_backend_cuda.cu +++ b/c/tests/test_backend_cuda.cu @@ -4,6 +4,7 @@ #include #include #include +#include #ifdef _WIN32 /* MSVC has no POSIX setenv/unsetenv */ @@ -29,6 +30,191 @@ static int relative_rms(const float *got,const float *want,int n,float limit){ if(r>limit){std::fprintf(stderr,"relative RMS %.5f exceeds %.5f\n",r,limit);return 0;} return 1; } +/* ---- fmt=6 (E8/IQ3) -------------------------------------------------------- + * The reference below is written from the format description, not shared with + * quant.h's decoder, so a common-mode mistake in one cannot hide in the other. + * A super-block is 98 bytes per 256 weights: 64 codebook indices, then 8 words + * of (4x7 sign bits + a 4-bit sub-scale in the top nibble), then an fp16 scale. + * Two indices feed each group of 8 weights; the 8th sign is the parity of the + * other 7. Scales live in the block, so fmt=6 tensors carry no scale array. */ +#define T6_QK 256 +#define T6_SUB 32 +#define T6_BB 98 + +static void t6_decode_row(const uint8_t *row, int I, float *w, + const uint8_t grid[256][4]) { + int nb = (I + T6_QK - 1) / T6_QK; + for (int b = 0; b < nb; b++) { + const uint8_t *blk = row + (size_t)b*T6_BB; + uint16_t h = (uint16_t)blk[96] | ((uint16_t)blk[97] << 8); + /* fp16 -> float, written out rather than reusing any helper */ + uint32_t sg=(uint32_t)(h&0x8000)<<16, ex=(h>>10)&0x1F, mn=h&0x3FF, bits; + if (!ex) bits = mn ? (sg|((127u-15u)<<23)|(mn<<13)) : sg; + else if (ex==31) bits = sg|0x7F800000u|(mn<<13); + else bits = sg|((ex+112u)<<23)|(mn<<13); + float d; std::memcpy(&d,&bits,4); + for (int ib = 0; ib < T6_QK/T6_SUB; ib++) { + int base = b*T6_QK + ib*T6_SUB; + if (base >= I) return; + const uint8_t *wp = blk + 64 + ib*4; + uint32_t word = (uint32_t)wp[0] | ((uint32_t)wp[1]<<8) | + ((uint32_t)wp[2]<<16) | ((uint32_t)wp[3]<<24); + float db = d * (0.5f + (float)(word >> 28)) * 0.5f; + for (int l = 0; l < 4; l++) { + uint32_t sev = (word >> (7*l)) & 0x7F; + const uint8_t *ga = grid[blk[ib*8 + l*2]]; + const uint8_t *gb = grid[blk[ib*8 + l*2 + 1]]; + int parity = 0; + for (int j = 0; j < 8; j++) { + int idx = base + l*8 + j; + if (idx >= I) break; + int neg; + if (j < 7) { neg = (sev >> j) & 1; parity ^= neg; } + else { neg = parity; } + float mag = (float)(j < 4 ? ga[j] : gb[j-4]) * 0.5f; + w[idx] = neg ? -mag*db : mag*db; + } + } + } + } +} + +/* Sign stream: xorshift64* seeded 417+n, one bit per element (quant.h's + * e8_signs regenerates the identical stream, and the converter drew it too). */ +static void t6_signs(uint8_t *bits, int n) { + uint64_t s = 417u + (uint64_t)n; + for (int i = 0; i < (n+7)/8; i++) { + s ^= s >> 12; s ^= s << 25; s ^= s >> 27; + bits[i] = (uint8_t)((s * 2685821657736338717ULL) >> 56); + } +} +/* y = Q^T x, Q = D*H/sqrt(n); non-power-of-two dims tile block-diagonally. */ +static void t6_rot(float *row, int dim) { + int off = 0; + while (off < dim) { + int rem = dim-off, n = rem & (-rem); + while (n > 4096) n >>= 1; + uint8_t bits[4096/8]; t6_signs(bits, n); + float *a = row + off; + for (int i = 0; i < n; i++) if (bits[i>>3]>>(i&7)&1) a[i] = -a[i]; + for (int len = 1; len < n; len <<= 1) + for (int i = 0; i < n; i += len<<1) + for (int j = i; j < i+len; j++) { + float u=a[j], v=a[j+len]; a[j]=u+v; a[j+len]=u-v; + } + float sc = 1.0f/std::sqrt((float)n); + for (int i = 0; i < n; i++) a[i] *= sc; + off += n; + } +} + +static uint32_t t6_rng_state = 0x2545F491u; +static uint32_t t6_rng(void){ t6_rng_state ^= t6_rng_state<<13; t6_rng_state ^= t6_rng_state>>17; + t6_rng_state ^= t6_rng_state<<5; return t6_rng_state; } + +/* Build a random-but-valid fmt=6 tensor: every index and sign pattern is legal, + * only the fp16 scale is constrained so the comparison stays meaningful. */ +static void t6_fill(uint8_t *q, int I, int O) { + int nb = (I + T6_QK - 1) / T6_QK; + for (int o = 0; o < O; o++) + for (int b = 0; b < nb; b++) { + uint8_t *blk = q + ((size_t)o*nb + b)*T6_BB; + for (int i = 0; i < 96; i++) blk[i] = (uint8_t)t6_rng(); + uint16_t h = (uint16_t)((t6_rng() & 0x03FF) | (uint32_t)((10 + t6_rng()%6) << 10)); + blk[96] = (uint8_t)(h & 0xFF); blk[97] = (uint8_t)(h >> 8); + } +} + +static int test_fmt6(int dev) { + /* A synthetic codebook: the engine passes quant.h's real table, but any 256x4 + * byte table is valid, and a synthetic one keeps this test self-contained + * while still exercising the upload path the real table travels. */ + static uint8_t grid[256][4]; + for (int i = 0; i < 256; i++) + for (int j = 0; j < 4; j++) grid[i][j] = (uint8_t)((i*4 + j) % 17); + if (!coli_cuda_e8_set_grid(grid)) { std::fprintf(stderr,"e8 grid upload failed\n"); return 0; } + + const int I = 256, O = 128, S = 2; + int nb = (I + T6_QK - 1) / T6_QK; + uint8_t *q = (uint8_t*)std::malloc((size_t)O*nb*T6_BB); + t6_fill(q, I, O); + float *x = (float*)std::malloc((size_t)S*I*sizeof(float)); + for (int i = 0; i < S*I; i++) x[i] = std::sin((float)(i+1)*0.031f); + + /* --- matmul --- */ + float *want = (float*)std::calloc((size_t)S*O, sizeof(float)); + float *wrow = (float*)std::malloc((size_t)I*sizeof(float)); + for (int o = 0; o < O; o++) { + t6_decode_row(q + (size_t)o*nb*T6_BB, I, wrow, grid); + for (int s = 0; s < S; s++) { + double acc = 0; + for (int i = 0; i < I; i++) acc += (double)x[s*I+i]*(double)wrow[i]; + want[s*O+o] = (float)acc; + } + } + float *got = (float*)std::malloc((size_t)S*O*sizeof(float)); + ColiCudaTensor *t6 = nullptr; + int ok = coli_cuda_matmul(&t6, got, x, q, nullptr, 6, S, I, O, dev, 0); + if (!ok) { std::fprintf(stderr,"fmt=6 matmul rejected\n"); return 0; } + if (!relative_rms(got, want, S*O, 1e-4f)) { std::fprintf(stderr,"fmt=6 matmul mismatch\n"); return 0; } + + /* --- expert MLP: gate/up, silu, the device-side down rotation, down --- + * The caller owns the gate/up input rotation (once per layer), so x goes in + * as-is; the backend must rotate the silu product before the down matmul. */ + uint8_t *qu = (uint8_t*)std::malloc((size_t)O*nb*T6_BB); + t6_fill(qu, I, O); + int nbd = (O + T6_QK - 1) / T6_QK; + uint8_t *qd = (uint8_t*)std::malloc((size_t)I*nbd*T6_BB); + t6_fill(qd, O, I); + + float *g = (float*)std::malloc((size_t)S*O*sizeof(float)); + float *u = (float*)std::malloc((size_t)S*O*sizeof(float)); + float *wr2 = (float*)std::malloc((size_t)O*sizeof(float)); + for (int o = 0; o < O; o++) { + t6_decode_row(q + (size_t)o*nb*T6_BB, I, wrow, grid); + float *wu = (float*)std::malloc((size_t)I*sizeof(float)); + t6_decode_row(qu + (size_t)o*nb*T6_BB, I, wu, grid); + for (int s = 0; s < S; s++) { + double a = 0, b = 0; + for (int i = 0; i < I; i++) { a += (double)x[s*I+i]*(double)wrow[i]; + b += (double)x[s*I+i]*(double)wu[i]; } + g[s*O+o] = (float)a; u[s*O+o] = (float)b; + } + std::free(wu); + } + for (int i = 0; i < S*O; i++) g[i] = (g[i]/(1.0f+std::exp(-g[i]))) * u[i]; + for (int s = 0; s < S; s++) t6_rot(g + (size_t)s*O, O); /* down input */ + float *want_e = (float*)std::calloc((size_t)S*I, sizeof(float)); + for (int o = 0; o < I; o++) { + t6_decode_row(qd + (size_t)o*nbd*T6_BB, O, wr2, grid); + for (int s = 0; s < S; s++) { + double acc = 0; + for (int i = 0; i < O; i++) acc += (double)g[s*O+i]*(double)wr2[i]; + want_e[s*I+o] = (float)acc; + } + } + ColiCudaTensor *tg6=nullptr,*tu6=nullptr,*td6=nullptr; + if (!coli_cuda_tensor_upload(&tg6,q, nullptr,6,I,O,dev) || + !coli_cuda_tensor_upload(&tu6,qu,nullptr,6,I,O,dev) || + !coli_cuda_tensor_upload(&td6,qd,nullptr,6,O,I,dev)) { + std::fprintf(stderr,"fmt=6 expert upload failed\n"); return 0; + } + float *got_e = (float*)std::malloc((size_t)S*I*sizeof(float)); + if (!coli_cuda_expert_mlp(tg6,tu6,td6,got_e,x,S)) { + std::fprintf(stderr,"fmt=6 expert_mlp rejected\n"); return 0; + } + if (!relative_rms(got_e, want_e, S*I, 2e-4f)) { + std::fprintf(stderr,"fmt=6 expert_mlp mismatch (device-side rotation?)\n"); return 0; + } + + coli_cuda_tensor_free(t6); coli_cuda_tensor_free(tg6); + coli_cuda_tensor_free(tu6); coli_cuda_tensor_free(td6); + std::free(q); std::free(qu); std::free(qd); std::free(x); std::free(want); + std::free(got); std::free(wrow); std::free(wr2); std::free(g); std::free(u); + std::free(want_e); std::free(got_e); + return 1; +} + int main(int argc, char **argv) { int devices[COLI_CUDA_MAX_DEVICES], ndev = argc > 1 ? argc - 1 : 1; if (ndev > COLI_CUDA_MAX_DEVICES) return 2; @@ -202,7 +388,13 @@ int main(int argc, char **argv) { coli_cuda_tensor_free(td); coli_cuda_stats(-1, &count, &bytes); if (count || bytes) return 1; + + /* fmt=6 runs after the stats assertions above, which pin exact tensor counts. */ + if (!test_fmt6(d0)) return 1; + coli_cuda_stats(-1, &count, &bytes); + if (count || bytes) { std::fprintf(stderr,"fmt=6 leaked tensors\n"); return 1; } + coli_cuda_shutdown(); - std::printf("cuda backend: q8/q4/q2/f32 correctness ok on %d device(s)\n", ndev); + std::printf("cuda backend: q8/q4/q2/f32/e8 correctness ok on %d device(s)\n", ndev); return 0; } From ab6c370483935f39bdb30a8416bb2c9d335ca2a8 Mon Sep 17 00:00:00 2001 From: Peter Groom Date: Sun, 26 Jul 2026 10:08:15 -0300 Subject: [PATCH 76/96] docs: add CUDA-backend build recipe for glibc >= 2.41 Building the CUDA backend on Debian trixie / Fedora 40+ etc. hits a glibc-2.41 sinpi/cospi exception-spec clash with CUDA <= 12.9. Documents the micromamba + CUDA 13.3 route that fixes it, and the dead ends (Debian nvidia-cuda-toolkit conflicts with a .run/dkms driver; the pip nvcc wheel ships only ptxas). --- docs/BUILD-cuda-glibc241.md | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 docs/BUILD-cuda-glibc241.md diff --git a/docs/BUILD-cuda-glibc241.md b/docs/BUILD-cuda-glibc241.md new file mode 100644 index 00000000..aa7a795d --- /dev/null +++ b/docs/BUILD-cuda-glibc241.md @@ -0,0 +1,33 @@ +# Building the CUDA backend on glibc ≥ 2.41 (Debian trixie and newer) + +**Author:** Peter Groom · 2026-07-18 + +Symptom: `make glm CUDA=1` fails with four errors like + + mathcalls.h(79): error: exception specification is incompatible with that of + previous function "cospi" (declared at ... crt/math_functions.h) + +Cause: glibc 2.41 added C23 `sinpi/cospi/sinpif/cospif` with exception specs that clash +with CUDA ≤ 12.9's `math_functions.h` declarations. **CUDA 13.x headers fix it.** + +Recipe that needs neither root-owned toolkits nor distro driver packages (safe next to a +`.run`-installed or dkms driver — important in LXC setups where userland must match the +host kernel module version): + +```bash +# micromamba (single static binary) + NVIDIA's conda channel = complete nvcc +curl -Ls https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xj -C /opt bin/micromamba +/opt/bin/micromamba create -y -p /opt/cuda13 -c nvidia -c conda-forge cuda-nvcc=13 cuda-cudart-dev=13 +ln -s /opt/cuda13/lib /opt/cuda13/lib64 # Makefile expects lib64; conda ships lib + +make glm CUDA=1 CUDA_ARCH=sm_86 CUDA_HOME=/opt/cuda13 # your arch here +``` + +The binary links `libcudart.so.13` dynamically — ship it next to the binary and run with +`LD_LIBRARY_PATH`, or drop it in `/usr/local/lib`. Driver ≥ the runtime's minimum is the +only host requirement (any recent driver runs CUDA 13 binaries). + +Dead ends, for the record: Debian's `nvidia-cuda-toolkit` hard-depends on Debian's driver +userland (version-clashes a `.run`/dkms driver — and its `nvidia-installer-cleanup` +postinst will try to delete your hand-installed userland); pip's `nvidia-cuda-nvcc-cu12` +wheel ships only `ptxas`, not the compiler frontend. From 455b071ebcae8168a7bc8c440958caf5ca34eecf Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Sun, 26 Jul 2026 23:43:13 +0800 Subject: [PATCH 77/96] cuda: keep async packed-int4 groups token exact --- c/backend_cuda.cu | 26 ++++++++++++++++++++++---- c/tests/test_backend_cuda.cu | 12 ++++++++++-- 2 files changed, 32 insertions(+), 6 deletions(-) diff --git a/c/backend_cuda.cu b/c/backend_cuda.cu index 5a739cd9..a85c7adb 100644 --- a/c/backend_cuda.cu +++ b/c/backend_cuda.cu @@ -1085,7 +1085,7 @@ extern "C" int coli_cuda_expert_group_issue(ColiCudaTensor *const *gates, if (!gates || !ups || !downs || !rows || !x || count < 1 || count > 64) return 0; ColiCudaTensor *first=gates[0]; if (!first) return 0; - int device=first->device,D=first->I,I=first->O,total=0; + int device=first->device,D=first->I,I=first->O,total=0,max_rows=0,all_s4=1; GroupDesc host[64]; for(int c=0;cweights,u->weights,d->weights,g->scales,u->scales,d->scales, g->fmt,u->fmt,d->fmt,rows[c],total, g->gs,u->gs,d->gs}; - total+=rows[c]; + all_s4&=g->fmt==2&&u->fmt==2&&d->fmt==2; + total+=rows[c]; if(rows[c]>max_rows) max_rows=rows[c]; } if(total>8) return 0; /* decode-scale only */ DeviceContext *ctx=find_ctx(device); if(!ctx||ctx->group_pending||!select_ctx(ctx)) return 0; size_t xb=(size_t)total*D*sizeof(float), ib=(size_t)total*I*sizeof(float); if(!reserve(&ctx->x,&ctx->x_cap,xb)||!reserve(&ctx->y,&ctx->y_cap,xb)|| !reserve(&ctx->gate,&ctx->gate_cap,ib)||!reserve(&ctx->up,&ctx->up_cap,ib)|| + !reserve_bytes(&ctx->group_desc,&ctx->group_desc_cap,(size_t)count*sizeof(GroupDesc))|| !reserve_pinned(&ctx->host_x,&ctx->host_x_cap,xb)|| !reserve_pinned(&ctx->host_y,&ctx->host_y_cap,xb)) return 0; std::memcpy(ctx->host_x,x,xb); - if(!cuda_ok(cudaMemcpyAsync(ctx->x,ctx->host_x,xb,cudaMemcpyHostToDevice,ctx->stream), + if(!cuda_ok(cudaMemcpyAsync(ctx->group_desc,host,(size_t)count*sizeof(GroupDesc), + cudaMemcpyHostToDevice,ctx->stream), + "expert group issue descriptors")|| + !cuda_ok(cudaMemcpyAsync(ctx->x,ctx->host_x,xb,cudaMemcpyHostToDevice,ctx->stream), "expert group issue upload")) return 0; - for(int c=0;cgroup_desc; + dim3 hg((unsigned)I,(unsigned)max_rows,(unsigned)count); + dim3 og((unsigned)D,(unsigned)max_rows,(unsigned)count); + int dual=!getenv("COLI_CUDA_DUAL_PROJ")||atoi(getenv("COLI_CUDA_DUAL_PROJ")); + if(dual) grouped_hidden_w4_dual<<stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D); + else { + grouped_hidden_w4<<stream>>>(ctx->gate,ctx->x,dev,I,D,0); + grouped_hidden_w4<<stream>>>(ctx->up,ctx->x,dev,I,D,1); + silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>( + ctx->gate,ctx->up,(size_t)total*I); + } + grouped_down_w4<<stream>>>(ctx->y,ctx->gate,dev,D,I); + } else for(int c=0;cgate+(size_t)host[c].offset*I,*u16=ctx->up+(size_t)host[c].offset*I; float *x16=ctx->x+(size_t)host[c].offset*D,*y16=ctx->y+(size_t)host[c].offset*D; diff --git a/c/tests/test_backend_cuda.cu b/c/tests/test_backend_cuda.cu index 0253cf60..f1537750 100644 --- a/c/tests/test_backend_cuda.cu +++ b/c/tests/test_backend_cuda.cu @@ -341,7 +341,7 @@ int main(int argc, char **argv) { /* Native s4 WMMA path: compare the quantized-activation result against the existing FP32-activation/s4-weight grouped implementation. */ - uint8_t w4[32*32/2]; float ws4[32], gx4[64], scalar4[64], tensor4[64]; + uint8_t w4[32*32/2]; float ws4[32], gx4[64], scalar4[64], async4[64], tensor4[64]; for(int i=0;i<(int)sizeof(w4);i++){ int lo=((i%15)-7)&15,hi=(((i*3)%15)-7)&15; w4[i]=(uint8_t)(lo|(hi<<4)); @@ -354,6 +354,14 @@ int main(int argc, char **argv) { !coli_cuda_tensor_upload_g(&d4,w4,ws4,2,32,32,d0,0))return 1; ColiCudaTensor *gg4[2]={g4,g4},*ug4[2]={u4,u4},*dg4[2]={d4,d4}; if(!coli_cuda_expert_group(gg4,ug4,dg4,group_rows,2,scalar4,gx4))return 1; + if(!coli_cuda_expert_group_issue(gg4,ug4,dg4,group_rows,2,gx4))return 1; + const float *async_result=coli_cuda_expert_group_take(d0); + if(!async_result)return 1; + std::memcpy(async4,async_result,sizeof(async4)); + if(std::memcmp(async4,scalar4,sizeof(async4))){ + std::fprintf(stderr,"async packed-s4 group differs from sync path\n"); + return 1; + } setenv("COLI_CUDA_TC_INT4","1",1); setenv("COLI_CUDA_TC_MIN_ROWS","1",1); if(!coli_cuda_expert_group(gg4,ug4,dg4,group_rows,2,tensor4,gx4)|| @@ -363,7 +371,7 @@ int main(int argc, char **argv) { coli_cuda_tensor_free(g4);coli_cuda_tensor_free(u4);coli_cuda_tensor_free(d4); uint64_t group_calls=0,group_experts=0,group_total_rows=0; coli_cuda_group_stats(&group_calls,&group_experts,&group_total_rows,nullptr,nullptr,nullptr); - if(group_calls!=3||group_experts!=6||group_total_rows!=6) return 1; + if(group_calls!=4||group_experts!=8||group_total_rows!=8) return 1; coli_cuda_stats(-1, &count, &bytes); if (count != 7 || bytes != 166) { From 6ccb65a1a95f627ee372ce75227ce634a201e103 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Mon, 27 Jul 2026 00:23:54 +0800 Subject: [PATCH 78/96] fix(chat): retain role stop tokens in private mode --- c/sample.h | 15 +++++++++------ c/tests/test_stops.c | 22 ++++++++++++++++++++++ 2 files changed, 31 insertions(+), 6 deletions(-) diff --git a/c/sample.h b/c/sample.h index 1df00ad4..3ca2c09f 100644 --- a/c/sample.h +++ b/c/sample.h @@ -116,20 +116,23 @@ static void stops_arm_tok(const Cfg *c, int tok_eos, Tok *T){ int nsp = 0; if (T) for (int id = 0; id < T->n_ids && g_nstop < 64; id++) if (T->id_special[id] && !is_stop(id)) { g_stop[g_nstop++] = id; nsp++; } - /* #401: in serve mode keep ONLY <|endoftext|>. Role markers <|user|>/<|observation|> - * (config stops + tokenizer special set) are boundaries the Python server owns; as - * hard stops they cut generation the moment the model opens a block, - * because int4 argmax noise picks a stop-token ID over the correct '<' token. + /* #401: in batched gateway mode keep ONLY <|endoftext|>. Role markers + * <|user|>/<|observation|> (config stops + tokenizer special set) are boundaries + * the Python server owns; as hard stops they cut generation the moment the model + * opens a block, because int4 argmax noise picks a stop-token ID over + * the correct '<' token. Private `coli chat` also sets SERVE=1 for the byte + * protocol, but has no Python StopFilter, so it must retain the full stop set. * * #549: on some quantized containers (notably int4-gs64) an end-of-turn special * OTHER than <|endoftext|> wins the final-token margin, so serve mode never stops and * runs into hallucinated turns. COLI_SERVE_ALL_STOPS=1 re-arms the full stop set for * users who are NOT doing tool calls (or whose client owns the tool boundary), at the * cost of the #401 tool-call safety. Default off — filter unchanged. */ - if (getenv("SERVE") && tok_eos >= 0 && !getenv("COLI_SERVE_ALL_STOPS")) { + if (getenv("SERVE") && getenv("SERVE_BATCH") && atoi(getenv("SERVE_BATCH")) && + tok_eos >= 0 && !getenv("COLI_SERVE_ALL_STOPS")) { int kept = 0; for (int i = 0; i < g_nstop; i++) if (g_stop[i] == tok_eos) g_stop[kept++] = g_stop[i]; - if (kept < g_nstop) fprintf(stderr, "[stop] serve mode: filtered %d non-EOS stop tokens (tool-call safety, #401)\n", g_nstop - kept); + if (kept < g_nstop) fprintf(stderr, "[stop] batched serve mode: filtered %d non-EOS stop tokens (tool-call safety, #401)\n", g_nstop - kept); g_nstop = kept; nsp = 0; } fprintf(stderr, "[stop] %d stop tokens:", g_nstop); diff --git a/c/tests/test_stops.c b/c/tests/test_stops.c index 5e7e9d2a..ad4e7d78 100644 --- a/c/tests/test_stops.c +++ b/c/tests/test_stops.c @@ -128,6 +128,28 @@ int main(void){ fail|=expect("T=NULL: no tokenizer sweep",101,0); if(!fail) printf(" T=NULL -> config stops only (validation path untouched) ok\n"); } + /* 6. Private chat uses SERVE=1 for the byte protocol but has no Python + * StopFilter. Only the batched gateway may reduce the engine stop set. */ + { Cfg c; memset(&c,0,sizeof c); c.stop_ids[0]=100; c.n_stop=1; + setenv("SERVE","1",1); unsetenv("SERVE_BATCH"); unsetenv("COLI_SERVE_ALL_STOPS"); + stops_arm_tok(&c,100,&T); + fail|=expect("private chat keeps <|user|>",101,1); + fail|=expect("private chat keeps <|observation|>",102,1); + if(g_nstop!=5){ fprintf(stderr," FAIL private chat: expected 5 stops, got %d\n",g_nstop); fail=1; } + + setenv("SERVE_BATCH","1",1); + stops_arm_tok(&c,100,&T); + fail|=expect("batched gateway keeps EOS",100,1); + fail|=expect("batched gateway filters <|user|>",101,0); + if(g_nstop!=1){ fprintf(stderr," FAIL batched gateway: expected 1 stop, got %d\n",g_nstop); fail=1; } + + setenv("COLI_SERVE_ALL_STOPS","1",1); + stops_arm_tok(&c,100,&T); + fail|=expect("gateway override restores <|user|>",101,1); + if(g_nstop!=5){ fprintf(stderr," FAIL gateway override: expected 5 stops, got %d\n",g_nstop); fail=1; } + unsetenv("COLI_SERVE_ALL_STOPS"); unsetenv("SERVE_BATCH"); unsetenv("SERVE"); + if(!fail) printf(" private chat keeps 5 stops; batched gateway filters to EOS ok\n"); } + rm_file(dir,"config.json"); rm_file(dir,"generation_config.json"); rm_file(dir,"tokenizer.json"); rmdir(dir); if(fail){ printf("test_stops: FAIL\n"); return 1; } From 3472d415e69f95852405e2be8001b86f91adb285 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Mon, 27 Jul 2026 00:28:47 +0800 Subject: [PATCH 79/96] test(stops): clear environment portably on Windows --- c/tests/test_stops.c | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/c/tests/test_stops.c b/c/tests/test_stops.c index ad4e7d78..b864660a 100644 --- a/c/tests/test_stops.c +++ b/c/tests/test_stops.c @@ -63,6 +63,14 @@ static int expect(const char *what, int id, int want){ return 0; } +static void clear_env(const char *name){ +#ifdef _WIN32 + SetEnvironmentVariableA(name,NULL); +#else + unsetenv(name); +#endif +} + int main(void){ int fail=0; /* Relative to the CWD, like test_compat_direct's TMPF — NOT "/tmp/...". @@ -131,7 +139,7 @@ int main(void){ /* 6. Private chat uses SERVE=1 for the byte protocol but has no Python * StopFilter. Only the batched gateway may reduce the engine stop set. */ { Cfg c; memset(&c,0,sizeof c); c.stop_ids[0]=100; c.n_stop=1; - setenv("SERVE","1",1); unsetenv("SERVE_BATCH"); unsetenv("COLI_SERVE_ALL_STOPS"); + setenv("SERVE","1",1); clear_env("SERVE_BATCH"); clear_env("COLI_SERVE_ALL_STOPS"); stops_arm_tok(&c,100,&T); fail|=expect("private chat keeps <|user|>",101,1); fail|=expect("private chat keeps <|observation|>",102,1); @@ -147,7 +155,7 @@ int main(void){ stops_arm_tok(&c,100,&T); fail|=expect("gateway override restores <|user|>",101,1); if(g_nstop!=5){ fprintf(stderr," FAIL gateway override: expected 5 stops, got %d\n",g_nstop); fail=1; } - unsetenv("COLI_SERVE_ALL_STOPS"); unsetenv("SERVE_BATCH"); unsetenv("SERVE"); + clear_env("COLI_SERVE_ALL_STOPS"); clear_env("SERVE_BATCH"); clear_env("SERVE"); if(!fail) printf(" private chat keeps 5 stops; batched gateway filters to EOS ok\n"); } rm_file(dir,"config.json"); rm_file(dir,"generation_config.json"); rm_file(dir,"tokenizer.json"); From 1c6fdab0350d3ddf9a41806fb78522c185243062 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Mon, 27 Jul 2026 00:36:45 +0800 Subject: [PATCH 80/96] test(stops): update the Windows CRT environment --- c/tests/test_stops.c | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/c/tests/test_stops.c b/c/tests/test_stops.c index b864660a..6aea6abe 100644 --- a/c/tests/test_stops.c +++ b/c/tests/test_stops.c @@ -63,9 +63,17 @@ static int expect(const char *what, int id, int want){ return 0; } +static void test_set_env(const char *name, const char *value){ +#ifdef _WIN32 + _putenv_s(name,value); +#else + setenv(name,value,1); +#endif +} + static void clear_env(const char *name){ #ifdef _WIN32 - SetEnvironmentVariableA(name,NULL); + _putenv_s(name,""); #else unsetenv(name); #endif @@ -139,19 +147,19 @@ int main(void){ /* 6. Private chat uses SERVE=1 for the byte protocol but has no Python * StopFilter. Only the batched gateway may reduce the engine stop set. */ { Cfg c; memset(&c,0,sizeof c); c.stop_ids[0]=100; c.n_stop=1; - setenv("SERVE","1",1); clear_env("SERVE_BATCH"); clear_env("COLI_SERVE_ALL_STOPS"); + test_set_env("SERVE","1"); clear_env("SERVE_BATCH"); clear_env("COLI_SERVE_ALL_STOPS"); stops_arm_tok(&c,100,&T); fail|=expect("private chat keeps <|user|>",101,1); fail|=expect("private chat keeps <|observation|>",102,1); if(g_nstop!=5){ fprintf(stderr," FAIL private chat: expected 5 stops, got %d\n",g_nstop); fail=1; } - setenv("SERVE_BATCH","1",1); + test_set_env("SERVE_BATCH","1"); stops_arm_tok(&c,100,&T); fail|=expect("batched gateway keeps EOS",100,1); fail|=expect("batched gateway filters <|user|>",101,0); if(g_nstop!=1){ fprintf(stderr," FAIL batched gateway: expected 1 stop, got %d\n",g_nstop); fail=1; } - setenv("COLI_SERVE_ALL_STOPS","1",1); + test_set_env("COLI_SERVE_ALL_STOPS","1"); stops_arm_tok(&c,100,&T); fail|=expect("gateway override restores <|user|>",101,1); if(g_nstop!=5){ fprintf(stderr," FAIL gateway override: expected 5 stops, got %d\n",g_nstop); fail=1; } From be79f4524ba84ba17e024b9f50c2f2aeb6becc4b Mon Sep 17 00:00:00 2001 From: duringcoffee Date: Sun, 26 Jul 2026 17:39:48 +0000 Subject: [PATCH 81/96] doctor: report shared libraries the engine cannot load An engine binary can be present and executable yet still fail to start when the loader cannot resolve one of its shared libraries. The engine exits before printing anything, so the caller sees only "colibri engine exited unexpectedly" and `coli doctor` reports engine.binary as pass. Hit this on AWS Graviton (aarch64). Prebuilt releases are x86_64, so ARM hosts build from source; moving that build to a freshly provisioned image without libgomp1 produces `libgomp.so.1 => not found` for every OpenMP build, with no diagnostic naming the cause. cuda_linkage() already runs ldd but greps only for libcudart. Generalize the same call: collect every "not found" entry and fail engine.binary with the library names when any exist. Returns empty where ldd is unavailable (musl, macOS), so no platform gains a false failure. make check: 213 tests, OK. --- c/doctor.py | 31 ++++++++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) diff --git a/c/doctor.py b/c/doctor.py index fa4d63c2..c141dae0 100644 --- a/c/doctor.py +++ b/c/doctor.py @@ -376,6 +376,28 @@ def cuda_linkage(engine_path): return {"linked": False, "missing": False} +def missing_shared_libraries(engine_path): + """Shared libraries the engine needs but the loader cannot resolve. + + A binary built elsewhere (a prebuilt release, a copied build, a disk moved to a + fresh host) can be present and executable yet still fail to start. The engine + exits before printing anything and the caller only sees "engine exited + unexpectedly", so name the unresolved libraries instead. Typical case: a minimal + image without libgomp1, where every OpenMP build reports + "libgomp.so.1 => not found". + """ + engine = Path(engine_path) + if os.name != "posix" or not engine.is_file(): + return [] + try: + result = subprocess.run(["ldd", str(engine)], capture_output=True, text=True, + timeout=3, check=False) + except (OSError, subprocess.SubprocessError): + return [] # no ldd (musl, macOS): cannot tell, so claim nothing + return sorted({line.split("=>")[0].strip() + for line in result.stdout.splitlines() if "not found" in line}) + + def run_doctor(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0, *, engine_path, available_memory=None, available_disk=None, gpus=None, linkage=None, deep=False, mirror_dir=None): @@ -419,7 +441,14 @@ def run_doctor(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0, *, else: engine_ok = engine.is_file() and os.access(engine, os.X_OK) if engine_ok: - checks.append(_check("engine.binary", "pass", "engine executable is ready", path=str(engine))) + unresolved = missing_shared_libraries(engine) + if unresolved: + checks.append(_check("engine.binary", "fail", + "engine cannot load: " + ", ".join(unresolved) + + " (install the runtime package, e.g. libgomp1, and retry)", + path=str(engine), missing=unresolved)) + else: + checks.append(_check("engine.binary", "pass", "engine executable is ready", path=str(engine))) elif engine.is_file(): checks.append(_check("engine.binary", "fail", "engine exists but is not executable", path=str(engine))) else: From 72d2b70f5a4dfd90ce3b78766182794afa928a49 Mon Sep 17 00:00:00 2001 From: duringcoffee Date: Sun, 26 Jul 2026 17:49:16 +0000 Subject: [PATCH 82/96] docs: tell ARM64 users the prebuilt Linux archive will not run MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit quickstart's shortcut offers a prebuilt archive for "Linux, macOS and Windows" and shows colibri-v1.1.0-linux-x86_64.tar.gz. The Linux archive is x86_64 only, so an aarch64 reader (Graviton, Ampere, Raspberry Pi, ARM VMs) follows the shortcut, unpacks a binary their machine cannot execute, and gets no hint why. Nothing in docs/ currently mentions building on ARM64. Say it plainly at the shortcut: on ARM64 Linux there is no shortcut, build from source. Note that no ARM-specific flags are needed, since the engine is portable C with OpenMP and no x86-only intrinsics. Also document the runtime libgomp dependency. An engine built elsewhere and moved to a machine that has never had a compiler — minimal cloud image, fresh container, volume reattached to a new instance — exits at startup with no message because libgomp.so.1 cannot be resolved. Installing libgomp1 alone is enough. Verified on AWS Graviton4 (r8g.16xlarge), Ubuntu 24.04 aarch64, gcc 13.3: ./setup.sh builds and self-tests with no source changes. --- docs/quickstart.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/docs/quickstart.md b/docs/quickstart.md index db22a235..f67853f1 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -42,6 +42,10 @@ CPU-only by default. > Build from source instead if you want the fastest binary for *your* CPU > (`ARCH=native` unlocks the vector instructions your chip actually has), or if > you plan to hack on the engine. +> +> **On ARM64 Linux (AWS Graviton, Ampere, Raspberry Pi, aarch64 VMs) there is no +> shortcut**: the published Linux archive is x86_64 only. Build from source — +> sections 1 and 2 work unchanged, and the engine needs no ARM-specific flags. ### Linux (Ubuntu / Debian) @@ -51,7 +55,16 @@ sudo apt install -y build-essential git python3 ``` `build-essential` gives you `gcc`, `make`, and OpenMP (libgomp) — everything the -engine needs. +engine needs. The same line works on aarch64: the engine is portable C with +OpenMP and no x86-only intrinsics, so `./setup.sh` builds it on ARM64 without +source changes or extra flags (verified on AWS Graviton4, Ubuntu 24.04, gcc 13). + +> **Moving a build to another machine?** The engine links `libgomp.so.1` at run +> time. A host that has never had a compiler installed — a minimal cloud image, a +> fresh container, a restored volume attached to a new instance — may not carry +> it, and the engine then exits at startup before printing anything. Install the +> runtime package alone (`sudo apt install -y libgomp1`); `coli doctor` names the +> missing library. ### Windows From adf6d688dbb191402abb54537f5ce3ee4b39b964 Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sun, 26 Jul 2026 20:31:03 +0200 Subject: [PATCH 83/96] fix(web): stop truncating reasoning at 512 tokens by default The dashboard defaulted maxTokens to 512 and capped the input at 4096, so a GLM-5.2 reasoning trace on any non-trivial prompt was cut mid-trace with no indication to the user (reported in #634). - default max output tokens 512 -> 4096 - raise the input ceiling 4096 -> 32768 so long reasoning is reachable - surface a 'Truncated' badge (with a tooltip pointing at the setting) when the stream finishes with finish_reason=length, so a cutoff is never silent Gateway/engine paths unchanged; this is dashboard-only. Co-Authored-By: Claude Opus 4.8 (1M context) --- web/src/App.tsx | 6 ++++-- web/src/i18n/en.ts | 2 ++ web/src/i18n/it.ts | 2 ++ web/src/i18n/zh-CN.ts | 2 ++ web/src/i18n/zh-TW.ts | 2 ++ web/src/index.css | 1 + 6 files changed, 13 insertions(+), 2 deletions(-) diff --git a/web/src/App.tsx b/web/src/App.tsx index 39c9f3b0..4f3dc535 100644 --- a/web/src/App.tsx +++ b/web/src/App.tsx @@ -12,6 +12,7 @@ import { Globe, HardDrive, KeyRound, + AlertTriangle, Layers, Link2, LoaderCircle, @@ -57,7 +58,7 @@ export default function App() { const [models, setModels] = useState([]) const [model, setModel] = useState(() => stored(localStorage, "colibri.model", "glm-5.2-colibri")) const [temperature, setTemperature] = useState(0.7) - const [maxTokens, setMaxTokens] = useState(512) + const [maxTokens, setMaxTokens] = useState(4096) const [thinking, setThinking] = useState(false) const [cacheSlot, setCacheSlot] = useState(0) const [conversations, setConversations] = useState>({ 0: [] }) @@ -299,7 +300,7 @@ export default function App() { {Array.from({ length: kvSlots }, (_, slot) => )} {t("sidebar.kvSessionHelp")} : null} - + @@ -329,6 +330,7 @@ export default function App() { {!loading && tokPerSec != null ? {t("topbar.tokPerSec", { n: tokPerSec.toFixed(1) })} : null} {!loading && ttft != null ? TTFT {(ttft/1000).toFixed(1)}s : null} {!loading && lastRun?.usage ? {lastRun.usage.prompt_tokens}→{lastRun.usage.completion_tokens} : null} + {!loading && lastRun?.finishReason === "length" ? {t("topbar.truncated")} : null} {lastRun?.queueWaitMs != null ? queue {Math.round(lastRun.queueWaitMs)}ms : null} {t("topbar.slot", { n: cacheSlot + 1 })} diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index f3cdd01f..158971d8 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -54,6 +54,8 @@ const en: Record = { "topbar.tokens": "{{n}} tokens", "topbar.tokPerSec": "{{n}} tok/s", "topbar.slot": "slot {{n}}", + "topbar.truncated": "Truncated", + "topbar.truncatedHelp": "The response hit the max output tokens limit and was cut off. Raise \"Max output tokens\" to see the full answer.", "topbar.clear": "Clear", // hero / empty state diff --git a/web/src/i18n/it.ts b/web/src/i18n/it.ts index de9fee8d..7f73e1e9 100644 --- a/web/src/i18n/it.ts +++ b/web/src/i18n/it.ts @@ -47,6 +47,8 @@ const it: Record = { "topbar.tokens": "{{n}} token", "topbar.tokPerSec": "{{n}} tok/s", "topbar.slot": "slot {{n}}", + "topbar.truncated": "Troncato", + "topbar.truncatedHelp": "La risposta ha raggiunto il limite di token di output ed e' stata tagliata. Aumenta \"Token di output massimi\" per vedere la risposta completa.", "topbar.clear": "Pulisci", "hero.title": "MOTORE COLIBRÌ", diff --git a/web/src/i18n/zh-CN.ts b/web/src/i18n/zh-CN.ts index fc0a42f5..97de86af 100644 --- a/web/src/i18n/zh-CN.ts +++ b/web/src/i18n/zh-CN.ts @@ -47,6 +47,8 @@ const zhCN: Record = { "topbar.tokens": "{{n}} tokens", "topbar.tokPerSec": "{{n}} tok/s", "topbar.slot": "槽位 {{n}}", + "topbar.truncated": "已截断", + "topbar.truncatedHelp": "回复达到最大输出 token 上限而被截断。请调高“最大输出 token 数”以查看完整回答。", "topbar.clear": "清空", "hero.title": "COLIBRÌ 引擎", diff --git a/web/src/i18n/zh-TW.ts b/web/src/i18n/zh-TW.ts index 6435be2b..c59fe94f 100644 --- a/web/src/i18n/zh-TW.ts +++ b/web/src/i18n/zh-TW.ts @@ -47,6 +47,8 @@ const zhTW: Record = { "topbar.tokens": "{{n}} tokens", "topbar.tokPerSec": "{{n}} tok/s", "topbar.slot": "插槽 {{n}}", + "topbar.truncated": "已截斷", + "topbar.truncatedHelp": "回應達到最大輸出 token 上限而被截斷。請提高「最大輸出 token 數」以查看完整答案。", "topbar.clear": "清除", "hero.title": "COLIBRÌ 引擎", diff --git a/web/src/index.css b/web/src/index.css index 7a5d5392..58c30e1a 100644 --- a/web/src/index.css +++ b/web/src/index.css @@ -105,6 +105,7 @@ button:focus-visible, input:focus-visible, textarea:focus-visible, select:focus- /* ---- rich metrics badges & animations ---- */ .badge-live { background: rgba(78,214,165,.15); border-color: rgba(78,214,165,.4); color: #4ed6a5; } .badge-speed { background: rgba(90,155,216,.12); border-color: rgba(90,155,216,.35); color: #5a9bd8; } +.badge-warn { background: rgba(230,170,60,.14); border-color: rgba(230,170,60,.4); color: #e6aa3c; } .flash { animation: flash 0.6s ease infinite alternate; } @keyframes flash { from { opacity: 1; } to { opacity: 0.3; } } .top-actions { display: flex; flex-wrap: wrap; gap: 6px; align-items: center; } From e8580e759a39428b81b52668739fe282c4744a0b Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sun, 26 Jul 2026 20:35:29 +0200 Subject: [PATCH 84/96] fix(serve): pin each conversation to a stable KV slot (#634) With --kv-slots > 1 and no client-supplied cache_slot, the scheduler admitted each request to min(free_slots), blind to which slot already held that conversation's KV cache. Under any interleaving of clients a turn could land on another conversation's slot and pay a full re-prefill (reported as Defect 1 in #634). Derive a stable slot from a key that does not change across a conversation's turns -- the leading system messages plus the first user message -- so every turn of one conversation routes to the same slot and reuses its warm prefix. Distinct conversations spread across slots; with more live conversations than slots, colliding ones degrade to the old re-prefill rather than to anything worse. Raw /v1/completions (no messages) and any client that pins cache_slot are unchanged. Gateway-only; the engine's per-slot KV reuse is untouched. Unit-tested in test_openai_server.py (stable across turns, in range, deterministic, spreads). Co-Authored-By: Claude Opus 4.8 (1M context) --- c/openai_server.py | 46 +++++++++++++++++++++++++++++++++++ c/tests/test_openai_server.py | 45 +++++++++++++++++++++++++++++++++- 2 files changed, 90 insertions(+), 1 deletion(-) diff --git a/c/openai_server.py b/c/openai_server.py index cf2f5279..5f78eed3 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -5,6 +5,7 @@ import codecs import collections import contextlib +import hashlib import json import math import mimetypes @@ -861,6 +862,37 @@ def parse_stop_sequences(body): return tuple(sequences) +def conversation_cache_slot(messages, kv_slots): + """Stable KV slot for a conversation so its turns reuse the same cached prefix. + + The chat APIs are stateless: every turn resends the whole history, and the engine + caches each KV slot's prefix. When the client does not pin a `cache_slot`, the + scheduler falls back to `min(free_slots)`, which is blind to which slot already + holds this conversation. Under any interleaving of clients a turn can then land on + another conversation's slot and force a full re-prefill (#634, Defect 1). Hashing a + key that stays constant across a conversation's turns — the leading system messages + plus the first user message, which never change once the conversation has started — + routes every turn of one conversation to the same slot. Distinct conversations + spread across slots; when there are more live conversations than slots, colliding + ones degrade to the old re-prefill behaviour rather than to anything worse. + + Returns a slot in [0, kv_slots). Falls back to 0 when there is nothing to key on. + """ + if kv_slots <= 1 or not isinstance(messages, list) or not messages: + return 0 + prefix = [] + for message in messages: + prefix.append(message) + if isinstance(message, dict) and message.get("role") == "user": + break # first user turn reached: the key is now stable for the whole conversation + try: + key = json.dumps(prefix, sort_keys=True, default=str) + except (TypeError, ValueError): + key = repr(prefix) + digest = hashlib.sha1(key.encode("utf-8", "replace")).digest() + return int.from_bytes(digest[:8], "big") % kv_slots + + def stop_policy(body, chat): sequences = parse_stop_sequences(body) ignore_leading = body.get("x_colibri_ignore_leading_stop", False) @@ -1670,6 +1702,13 @@ def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=Non not 0 <= cache_slot < self.server.kv_slots)): raise APIError(400, f"`cache_slot` must be an integer between 0 and {self.server.kv_slots - 1}.", "cache_slot") + if cache_slot is None and self.server.kv_slots > 1: + # #634: pin each conversation to a stable KV slot so multi-turn reuses its + # cached prefix instead of re-prefilling. Only when the request carries a + # conversation; raw /v1/completions keeps the scheduler's free-slot pick. + conversation = body.get("messages") + if isinstance(conversation, list) and conversation: + cache_slot = conversation_cache_slot(conversation, self.server.kv_slots) stream = body.get("stream", False) if not isinstance(stream, bool): raise APIError(400, "`stream` must be a boolean.", "stream") @@ -1999,6 +2038,13 @@ def anthropic_generation(self, body, prompt, request_id, tools, enable_thinking) not 0 <= cache_slot < self.server.kv_slots)): raise APIError(400, f"`cache_slot` must be an integer between 0 and {self.server.kv_slots - 1}.", "cache_slot") + if cache_slot is None and self.server.kv_slots > 1: + # #634: pin each conversation to a stable KV slot so multi-turn reuses its + # cached prefix instead of re-prefilling. Only when the request carries a + # conversation; raw /v1/completions keeps the scheduler's free-slot pick. + conversation = body.get("messages") + if isinstance(conversation, list) and conversation: + cache_slot = conversation_cache_slot(conversation, self.server.kv_slots) stream = body.get("stream", False) if not isinstance(stream, bool): raise APIError(400, "`stream` must be a boolean.", "stream") diff --git a/c/tests/test_openai_server.py b/c/tests/test_openai_server.py index fb8235a3..2bda0cc7 100644 --- a/c/tests/test_openai_server.py +++ b/c/tests/test_openai_server.py @@ -14,7 +14,8 @@ from openai_server import (APIError, APIHandler, APIServer, ClientCancelled, DEFAULT_CHAT_STOP_SEQUENCES, END, GenerationScheduler, READY, Engine, InklingStreamSplit, StopFilter, ThinkingStreamSplit, - _engine_error, generation_options, parse_tool_calls, read_engine_turn, + _engine_error, conversation_cache_slot, generation_options, + parse_tool_calls, read_engine_turn, render_chat, serve, split_thinking_reply, stop_policy) @@ -1425,5 +1426,47 @@ def test_non_streaming_response_still_reuses_the_connection(self): self.assertIsNotNone(conn.sock, "the JSON path should keep the connection open") +class ConversationCacheSlotTest(unittest.TestCase): + """#634 Defect 1: a conversation must map to one stable KV slot across its turns.""" + + def _conv(self, *user_and_assistant_turns, system="you are a helpful assistant"): + messages = [{"role": "system", "content": system}] + for i, text in enumerate(user_and_assistant_turns): + messages.append({"role": "user" if i % 2 == 0 else "assistant", "content": text}) + return messages + + def test_single_slot_is_always_zero(self): + self.assertEqual(conversation_cache_slot(self._conv("hi"), 1), 0) + + def test_empty_or_bad_input_is_zero(self): + self.assertEqual(conversation_cache_slot(None, 8), 0) + self.assertEqual(conversation_cache_slot([], 8), 0) + + def test_slot_is_in_range(self): + for kv in (2, 3, 8, 16): + slot = conversation_cache_slot(self._conv("solve x"), kv) + self.assertTrue(0 <= slot < kv, f"slot {slot} out of range for kv={kv}") + + def test_stable_across_turns_of_one_conversation(self): + # The engine caches the prefix; every turn of the same conversation must return + # the same slot so it lands on its warm KV instead of re-prefilling. + first_turn = self._conv("1+1=") + second_turn = self._conv("1+1=", "2", "and 2+2?") + third_turn = self._conv("1+1=", "2", "and 2+2?", "4", "thanks") + base = conversation_cache_slot(first_turn, 8) + self.assertEqual(conversation_cache_slot(second_turn, 8), base) + self.assertEqual(conversation_cache_slot(third_turn, 8), base) + + def test_distinct_conversations_can_differ(self): + # Not a guarantee for any single pair (hashing collides sometimes), but across a + # spread of openings we must see more than one slot used, i.e. not everything on 0. + slots = {conversation_cache_slot(self._conv(f"task number {i}"), 8) for i in range(40)} + self.assertGreater(len(slots), 1) + + def test_deterministic(self): + conv = self._conv("same question", "same answer", "again") + self.assertEqual(conversation_cache_slot(conv, 8), conversation_cache_slot(conv, 8)) + + if __name__ == "__main__": unittest.main() From 259f3cbd6ae94bc59f7c5bb10904b8e6e8565973 Mon Sep 17 00:00:00 2001 From: ZacharyZcR Date: Mon, 27 Jul 2026 03:51:45 +0800 Subject: [PATCH 85/96] release: ship the web dashboard, so the Expert Atlas reaches users The README leads with a dashboard screenshot and the site's whole argument is the measured Expert Atlas, but no release archive has ever contained either. `release.yml` packages the engine and four .py files; web/dist is a build artifact and is not tracked, so a release user has no web/ to build from -- and `coli web` told them to "cd web && npm install" in a directory the archive does not have. #578 shipped web/public/experts.json, which is correct and does land in web/dist at build time. It just never reached anyone who did not build the front end themselves. Packaging alone would not have been enough: WEB_DIST was a single path, __file__/../../web/dist, which only resolves in a source checkout, where this file sits in c/. In an archive or an installed tree the server sits NEXT TO web/dist, one level nearer. Both openai_server.py and coli now probe for index.html across the two layouts instead of assuming one. The dashboard is built in CI rather than committed, so dist/ stays an artifact. On Windows the step forces shell: bash -- the job default is msys2, whose minimal PATH does not see the node that setup-node installs. `make install` installs web/dist next to openai_server.py when it exists, and says how to build it when it does not. The archive verification gains the dashboard: not just that the files are present, but that the packaged server actually RESOLVES them -- an archive with the right files under an unresolvable path is precisely the failure that step already exists to catch. Verified by replaying all three layouts locally: source checkout, an unpacked release tree, and make install DESTDIR -- each resolves to its own web/dist with index.html and experts.json present, and an unbuilt tree still falls through to the "web UI not built" hint. make check 211. Refs #578, #175 --- .github/workflows/release.yml | 30 ++++++++++++++++++++++++++++++ c/Makefile | 9 +++++++++ c/coli | 6 +++++- c/openai_server.py | 11 ++++++++++- 4 files changed, 54 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 88ae7e39..8c424ef6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -55,6 +55,26 @@ jobs: make colibri ${{ matrix.make_args }} ls -lh colibri${{ matrix.ext }} + - uses: actions/setup-node@v4 + with: + node-version: '20' + cache: npm + cache-dependency-path: web/package-lock.json + + # The dashboard is what README shows and what serves the Expert Atlas, but it + # was never packaged: a release user has no web/ to build from, so `coli web` + # pointed them at a directory the archive does not contain. Built here rather + # than committed, so dist/ stays a build artifact. + # shell: bash — on Windows the job default is msys2, but setup-node installs + # the native node/npm, which msys2's minimal PATH does not see. + - name: Build web dashboard + shell: bash + run: | + cd web + npm ci + npm run build + ls -lh dist/index.html dist/experts.json + - name: Package run: | TAG=${GITHUB_REF#refs/tags/} @@ -71,6 +91,10 @@ jobs: cp c/resource_plan.py dist/ cp c/doctor.py dist/ cp LICENSE dist/ + # web/dist sits NEXT TO coli in the archive; both coli and openai_server.py + # probe that layout as well as the source checkout's one-level-up form. + mkdir -p dist/web + cp -r web/dist dist/web/dist cd dist if [ "${{ matrix.ext }}" = ".exe" ]; then 7z a colibri-${TAG}-${{ matrix.name }}.zip * @@ -100,6 +124,12 @@ jobs: # (The v1.1.0 tag build failed here when the banner still said "colibrì".) echo "$out" | grep -q "colibri" || { echo "FAIL: coli did not run"; exit 1; } echo "OK: coli locates the engine in the published archive" + # Same reasoning for the dashboard: assert the files are there AND that the + # server resolves them, since a right-looking archive with an unresolvable + # path is exactly the failure this whole step exists to catch. + test -f web/dist/index.html || { echo "FAIL: dashboard missing from archive"; exit 1; } + test -f web/dist/experts.json || { echo "FAIL: expert atlas missing from archive"; exit 1; } + python3 -c "import sys; sys.path.insert(0, '.'); from openai_server import APIHandler as A; d = A.WEB_DIST; assert (d / 'index.html').is_file(), 'server resolved WEB_DIST to %s, which has no index.html' % d; assert (d / 'experts.json').is_file(), 'no expert atlas under %s' % d; print('OK: the packaged server resolves the dashboard at', d)" # Only the archives -- never the loose files. `dist/colibri-*.*` used to work by accident # (the engine was versioned, so it did not match); now that the engine is plainly named, diff --git a/c/Makefile b/c/Makefile index f29e498c..a570b5a5 100644 --- a/c/Makefile +++ b/c/Makefile @@ -514,6 +514,15 @@ install: colibri$(EXE) olmoe$(EXE) $(INSTALL) -m 755 olmoe$(EXE) $(DESTDIR)$(LIBEXECDIR)/olmoe$(EXE) $(INSTALL) -m 644 resource_plan.py doctor.py openai_server.py version.py $(DESTDIR)$(LIBEXECDIR)/ $(INSTALL) -m 644 tools/*.py $(DESTDIR)$(LIBEXECDIR)/tools/ + @# The dashboard is an optional build artifact (cd web && npm run build), so install + @# it only when it exists. It goes NEXT TO openai_server.py, which probes ./web/dist. + @if [ -f ../web/dist/index.html ]; then \ + $(INSTALL) -d $(DESTDIR)$(LIBEXECDIR)/web/dist; \ + cp -R ../web/dist/. $(DESTDIR)$(LIBEXECDIR)/web/dist/; \ + echo "installed the web dashboard"; \ + else \ + echo "web/dist absent -- skipping the dashboard (build it with: cd web && npm run build)"; \ + fi uninstall: rm -f $(DESTDIR)$(BINDIR)/coli diff --git a/c/coli b/c/coli index d9b13e6b..9f6cf667 100755 --- a/c/coli +++ b/c/coli @@ -897,7 +897,11 @@ def cmd_stop(a): def cmd_web(a): """serve + open the dashboard in the browser once the API answers.""" need_model(a.model) - dist = os.path.join(os.path.dirname(os.path.abspath(HERE)), "web", "dist") + # Same two layouts openai_server.py resolves: next to this script (release + # archive / installed tree) or one level up (source checkout, where this is c/coli). + cands = [os.path.join(HERE, "web", "dist"), + os.path.join(os.path.dirname(os.path.abspath(HERE)), "web", "dist")] + dist = next((d for d in cands if os.path.exists(os.path.join(d, "index.html"))), cands[-1]) if not os.path.exists(os.path.join(dist, "index.html")): print(f"{C.yel}web UI not built:{C.r} run cd web && npm install && npm run build first;") print("serving the API anyway (the dashboard will 404 until built).") diff --git a/c/openai_server.py b/c/openai_server.py index 5f78eed3..8b4dfaab 100644 --- a/c/openai_server.py +++ b/c/openai_server.py @@ -1534,7 +1534,16 @@ def check_model(self, body): if model != self.server.model_id: raise APIError(404, f"The model `{model}` does not exist.", "model", "model_not_found") - WEB_DIST = Path(__file__).resolve().parent.parent / "web" / "dist" + # The dashboard ships in two layouts and the old single path only knew one: + # a source checkout puts this file in c/ (so web/dist is one level UP), while + # a release archive and an installed tree put it next to web/dist. Probing for + # index.html rather than the directory keeps an empty leftover web/dist from + # shadowing a real one. + WEB_DIST = next( + (c for c in (Path(__file__).resolve().parent / "web" / "dist", + Path(__file__).resolve().parent.parent / "web" / "dist") + if (c / "index.html").is_file()), + Path(__file__).resolve().parent.parent / "web" / "dist") def serve_static(self, path): """Serve the built web UI (web/dist) so `coli web` is one process. From 2278a08e3410a043110691486b97ea82852ad389 Mon Sep 17 00:00:00 2001 From: JustVugg Date: Sun, 26 Jul 2026 23:35:24 +0200 Subject: [PATCH 86/96] docs: point the README to the gs64 model container (#642) The README still sent newcomers to the older per-row int4 mirror (mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp), which measures ~9pp worse on quality and causes the think-mode loops / never-terminating generations in #455. Point to the group-scaled (gs64) container that docs/quickstart.md already recommends, and explain why gs64 over per-row. Updates the English README plus the zh-CN / zh-TW / it variants and both docker READMEs. The historical experiment log keeps its original link (it records what was actually run). New: https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp Co-Authored-By: Claude Opus 4.8 (1M context) --- README.it.md | 2 +- README.md | 20 ++++++++++++-------- README.zh-CN.md | 2 +- README.zh-TW.md | 2 +- docker/README.IT.md | 6 +++--- docker/README.md | 6 +++--- 6 files changed, 21 insertions(+), 17 deletions(-) diff --git a/README.it.md b/README.it.md index e59eec84..09292357 100644 --- a/README.it.md +++ b/README.it.md @@ -198,7 +198,7 @@ Un container **GLM-5.2 int4** pre-convertito è su Hugging Face — **usa la versione con le teste MTP int8**. Pesa circa **372 GB**, quindi mettilo su un disco che abbia lo spazio, meglio se veloce: -**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp** +**https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp** > ⚠️ Il mirror originale contiene teste MTP int4 → accettazione dei draft allo 0% > ([#8](https://github.com/JustVugg/colibri/issues/8)). Verifica la tua versione: diff --git a/README.md b/README.md index 4b6d6922..a17696c3 100644 --- a/README.md +++ b/README.md @@ -236,14 +236,18 @@ engine still lives in `c/` — an editable install from the clone, not a wheel). ### 2. Get the model -A pre-converted **GLM-5.2 int4** container is on Hugging Face — **use the -version with the int8 MTP heads**. It is about **372 GB**, so put it on a disk -with the room, ideally a fast one: - -**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp** - -> ⚠️ The original mirror ships int4 MTP heads → 0% draft acceptance -> ([#8](https://github.com/JustVugg/colibri/issues/8)). Check yours: +A pre-converted **GLM-5.2 int4** container is on Hugging Face — use the +**group-scaled (gs64)** build with the **int8 MTP head**. It is about **372 GB**, +so put it on a disk with the room, ideally a fast one: + +**https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp** + +> ⚠️ Use the **gs64** container above, not the older per-row int4 mirrors +> (`mateogrgic/…`, `jlnsrk/…`): those measure ~9pp worse on quality and are the +> root cause of the think-mode loops and never-terminating generations in +> [#455](https://github.com/JustVugg/colibri/issues/455) — the gs64 container +> cured every failing case there. The MTP head must also be **int8, not int4** +> (int4 → 0% draft acceptance, [#8](https://github.com/JustVugg/colibri/issues/8)): > `ls -l /out-mtp-*` — int8 (correct) is `3527131672 / 5366238584 / 1065950496`. Or convert from the FP8 source yourself — one resumable command that never needs diff --git a/README.zh-CN.md b/README.zh-CN.md index 90b1cfd4..12e57e5b 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -178,7 +178,7 @@ git clone https://github.com/JustVugg/colibri && cd colibri/c Hugging Face 上已有预转换的 **GLM-5.2 int4** 容器——请务必使用 **含 int8 MTP head 的版本**。它约为 **372 GB**，请放在空间足够的磁盘上，最好是快盘： -**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp** +**https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp** > ⚠️ 原始镜像使用 int4 MTP head → 草稿接受率为 0% >（[#8](https://github.com/JustVugg/colibri/issues/8)）。请检查你的版本： diff --git a/README.zh-TW.md b/README.zh-TW.md index 55d5a3d0..4fe6e632 100644 --- a/README.zh-TW.md +++ b/README.zh-TW.md @@ -178,7 +178,7 @@ git clone https://github.com/JustVugg/colibri && cd colibri/c Hugging Face 上已有預先轉換的 **GLM-5.2 int4** 容器——請務必使用 **含 int8 MTP head 的版本**。它約為 **372 GB**，請放在空間足夠的硬碟上，最好是快碟： -**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp** +**https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp** > ⚠️ 原始鏡像使用 int4 MTP head → 草稿接受率為 0% >（[#8](https://github.com/JustVugg/colibri/issues/8)）。請檢查你的版本： diff --git a/docker/README.IT.md b/docker/README.IT.md index 302f0180..6acbbe7b 100644 --- a/docker/README.IT.md +++ b/docker/README.IT.md @@ -78,7 +78,7 @@ Il modello GLM 5.2 è circa **360 GB**. Scegli uno di questi metodi: 2. **Scarica il modello** (apri il terminale nella cartella dove lo vuoi salvare): ```bash - hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir . + hf_download mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp --local-dir . ``` **Esempio**: se vuoi salvarlo in `C:\LLM\models\glm-5.2` (Windows): @@ -89,7 +89,7 @@ Il modello GLM 5.2 è circa **360 GB**. Scegli uno di questi metodi: #### Metodo B: Senza Python (solo se necessario) Se sei su Windows e non riesci con Python: -- Scarica manualmente da [Hugging Face](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +- Scarica manualmente da [Hugging Face](https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp) - Decomprimi in una cartella (es. `C:\LLM\models\glm-5.2`) --- @@ -347,7 +347,7 @@ Poi riprova il comando `hf_download`. **Soluzione**: 1. Attendi e riprova il comando `hf_download` -2. Se continua, scarica manualmente da [qui](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +2. Se continua, scarica manualmente da [qui](https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp) 3. Decomprimi il file ZIP nella cartella desiderata --- diff --git a/docker/README.md b/docker/README.md index aed1a574..1e171dc2 100644 --- a/docker/README.md +++ b/docker/README.md @@ -78,7 +78,7 @@ python -m pip install -U huggingface_hub[cli] On Linux, use `python3` instead of `python`. 2. **Download the model** (open the terminal in the folder where you want to save it): ```bash -hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir . +hf_download mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp --local-dir . ``` **Example**: if you want to save it in `C:\LLM\models\glm-5.2` (Windows): @@ -90,7 +90,7 @@ hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir . If you are on Windows and cannot get it to work with Python: -* Download manually from [Hugging Face](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +* Download manually from [Hugging Face](https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp) * Unzip into a folder (e.g., `C:\LLM\models\glm-5.2`) --- @@ -371,7 +371,7 @@ Then retry the `hf_download` command. **Solution**: 1. Wait and retry the `hf_download` command -2. If it continues, download manually from [here](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp) +2. If it continues, download manually from [here](https://huggingface.co/mastouri/GLM-5.2-colibri-int4-g64-with-int8-mtp) 3. Unzip the ZIP file into the desired folder --- From 227dd8a6ff34f02290f2d742145fadf2a8ce522a Mon Sep 17 00:00:00 2001 From: JustVugg Date: Mon, 27 Jul 2026 00:19:57 +0200 Subject: [PATCH 87/96] site: new landing page (Anthropic-style) Replace the project website with a cleaner, editorial landing: warm cream + tan cards + near-black blocks, bold-sans headings with a serif for supporting copy, left-aligned hero, model cards with metadata rows, a hardware ladder, and a monochrome rotating expert-atlas galaxy drawn on canvas. Pixel wordmark in Bytesized to match the pixel hummingbird mark. Self-contained (fonts from Google Fonts, no images/assets), ~28 KB. Deployed to Pages by site.yml on main. Co-Authored-By: Claude Opus 4.8 (1M context) --- site/index.html | 968 ++++++++++++++++-------------------------------- 1 file changed, 324 insertions(+), 644 deletions(-) diff --git a/site/index.html b/site/index.html index 8070bee9..b253806c 100644 --- a/site/index.html +++ b/site/index.html @@ -3,695 +3,375 @@ -colibrì — tiny engine, immense model - - - - +colibri — a 744B model on hardware you own + + + + + + + -