10 Commits

Author SHA1 Message Date
FABIOTESS 84514a5f39 review: docs section, 1 MiB grammar pre-check, negative tests, measured compile overhead
Addresses the #192 review: server usage documented in docs/grammar-draft.md
(incl. back-compat statement for the additive SUBMIT field and the #100-class
near-tie caveat); gateway pre-checks grammar payloads at 1 MiB (matching the
engine's gbytes bound); negative tests for non-dict response_format, empty and
oversized grammars, plus an explicit test that malformed GBNF passes the
gateway by design (engine fail-soft, draft-source semantics). Measured compile
overhead: 7.8 us/request typical schema, 17.9 us at the 32-level nesting cap.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-20 17:27:33 +01:00
FABIOTESS d7b855f43f serve stage 2: per-request grammars end-to-end — response_format -> SUBMIT -> grammar-forced drafts in the multiplexed server
The OpenAI gateway previously 400'd every response_format; the mux engine path
ran with speculation disabled entirely. Now:

- openai_server.py: response_format {"type":"json_object"} (generic ws-tolerant
  JSON grammar), {"type":"json_schema"} (schema forwarded as-is, compiled
  engine-side by schema_gbnf.h), and a raw-GBNF {"type":"gbnf"} extension.
  Draft-source semantics throughout: a schema the engine cannot compile costs
  the speedup, never the request and never the output.
- SUBMIT protocol: optional 7th field gbytes; grammar text appended to the
  payload after the prompt. 6-field headers unchanged (back-compatible).
- Engine: per-slot GrDraft (grammar_setup_text/grammar_teardown split out of
  the env-driven setup); walkers fed on every emitted token. Grammar-forced
  drafting in run_serve_mux for greedy requests: a drafting slot leaves the
  shared batch for one forward and runs the proven single-sequence verify path
  (kv_bind + step_all) — the same primitives prefill already uses per
  submission — then rejoins; rejected drafts' KV entries are overwritten by the
  next forward exactly like the existing prefix-truncation path. Sampling
  requests never draft (verification under sampling needs rejection resampling;
  out of scope).

Tests: 7-field SUBMIT parse cases; response_format->grammar plumbing incl.
fail cases; test doubles updated; generic JSON grammar parse+walk validated
against grammar.h. make test-c green; python suite green except the known
environmental memory_available failure (#150 fixes it).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-20 17:27:33 +01:00
Claude 6afffbcbf2 Profiling page: per-turn phase timings, live in the web dashboard
The engine already tracks where each turn's wall time goes (expert-disk
service, I/O wait, expert matmul, attention, lm_head) — it just only spoke
at exit or under PROF=1. Stream it instead:

- glm.c: mux serve emits a per-turn "PROF" protocol line next to TIERS/HITS
  (window deltas per request, same convention as the STAT hit%); the phase
  window base is now always captured (a few loads per request).
- openai_server.py: parses PROF into a 120-turn rolling window and serves it
  at /profile (read-only, same trust level as /health).
- web: new Profiling tab — stat tiles (tok/s, wall, tokens/forward, disk
  service), wall-time composition bars for the last turn and the window,
  per-turn throughput and stacked phase columns with hover readouts, and a
  table of recent turns. Disk service is shown apart from the stack: it
  overlaps with compute, so only the I/O wait the compute thread felt counts
  inside wall time. Phase colours are a CVD-validated set with gaps + legend
  + table so identity never rides on colour alone.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WhTmF8yvEBgSkUKSVfZF7P
2026-07-16 08:56:12 -04:00
JustVugg da260c38cb server: clamp max_tokens to the server cap instead of rejecting it (fixes #260)
opencode / the ai-sdk OpenAI-compatible client sends a large default max_tokens
(> the server's --max-tokens cap, 1024 by default), and generation_options
returned 400 "must be an integer between 1 and 1024" — even for a trivial
"hello". OpenAI-compatible servers clamp to their own ceiling rather than
reject. Now max_tokens > limit is clamped to limit; only non-int / non-positive
values are a hard error. Test updated to assert the clamp + keep the <1 reject.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-15 14:56:01 +02:00
Kushida 8c18b7af68 openai_server: fix path traversal in web static serving (relative_to, not startswith) + empty-prompt validation, with tests (#212) 2026-07-14 22:07:41 +02:00
Dennis Paul 15bf8cac6b openai server: type tool arguments from schema + implement tool_choice, 9 new tool-calling tests (#118)
Two OpenAI-compat tool-calling bugs found against the real GLM-5.2 (dnnspaul): (1) string-typed args coerced to numbers — declared schema type now decides, string kept verbatim, bool rejected as number, schema-less params keep permissive decoding; (2) tool_choice was ignored — none/auto/required/{function} now honored, invalid returns 400. Python-only (openai_server.py + tests), engine untouched. 36/36 tests pass (verified independently in a clean worktree).
2026-07-13 16:11:31 +02:00
ZacharyZcR cbd599024e Unify continuous batching + heterogeneous runtime: decode batching, physical-core planning, disjoint VRAM/RAM placement, topp-policy warning (CPU-validated, CUDA on 6x5090) (#68)
* Fuse CUDA expert MLP execution

* Group CUDA expert transfers by device

* Instrument grouped CUDA expert execution

* Bound grouped CUDA decode scratch

* Execute expert groups across GPUs in parallel

* Release host backing for multi-GPU experts

* Define quality-preserving memory policies

* Overlap cold expert loading with resident compute

* Adapt expert placement with session LFRU

* Fuse q4 expert gate and up dispatch

* Plan CPU work on physical cores

* Batch grouped expert CUDA kernels

* Separate VRAM and RAM expert placement

* Add ragged multi-sequence decode forward

* feat(runtime): add continuous decode scheduler

* Route concurrent API requests through batch scheduler

* Harden multiplex request lifecycle and framing

* Cancel disconnected multiplex requests

* Bind API port before starting the engine

* fix automatic KV slot allocation

* add native int4 Tensor Core grouped GEMM

* add Tensor Core throughput benchmark

* optimize packed int4 low-row kernels

* add asynchronous CUDA staging streams

* document validated six-GPU dense acceleration

* tune six-GPU expert hot set

* raise validated expert hot-set target

* add CUDA MLA absorption core

* fuse grouped expert gate and up projections

* Warn for explicit lossy routing flags
2026-07-13 14:30:36 +02:00
ZacharyZcR 9e6e1ac327 Isolated sequence KV contexts: KVState extraction, KV_SLOTS serve contexts with per-slot persistence, budget-aware pool accounting (#29)
* Add isolated sequence KV contexts

* Log projected multi-slot KV footprint
2026-07-10 16:19:43 +02:00
ZacharyZcR 76e858a80f Bounded FIFO inference scheduling: admission queue, OpenAI-shaped 429/503, queued-disconnect detection (#28) 2026-07-10 12:52:07 +02:00
ZacharyZcR 3e4d08b6bf OpenAI-compatible HTTP API: stdlib-only gateway over SERVE with KV prefix reuse across stateless requests (#21)
* Add OpenAI-compatible HTTP API

* Support browser API clients

* Handle missing KV cache during rewind
2026-07-10 11:04:56 +02:00