Commit Graph

2 Commits

Author SHA1 Message Date
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
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