Three related hardening changes for the API surface:
- validate_request rejects NaN/negative temperature, out-of-range top_p,
and absurd top_k before those values reach the CUDA sampling paths.
Prevents NaN logits from downstream sampling and matches typical
OpenAI-compatible server behavior (400 instead of 500).
- normalize_finish_reason maps engine strings to the OpenAI-standard
subset. Currently only "error" (from tp/pp engine client-stall) needs
normalization — it collapses to null so SDK clients see a clean stream
close instead of an unknown finish_reason value. Applied to both
streaming (SSE) and non-streaming JSON responses.
- Replace the unbounded std::sync::mpsc engine channel with a bounded
sync_channel(256) and switch submit_to_engine to try_send. A saturated
engine now returns 503 "engine is busy" instead of letting requests
pile up in RAM. Also add axum DefaultBodyLimit(4 MiB) so a malicious
or misbehaving client cannot exhaust memory with an arbitrary JSON POST.
- --pp with gpt-oss now fails with a clear message instead of a
cryptic missing-weight panic inside the Qwen3-only PP engine.
- Sparse GEMV wrappers assert K%16==0 (FP8) / K%32==0 (MXFP4) — the
uint4-vectorized kernels would silently drop a tail otherwise.
- Document the topk_ids buffer holding i32 under an F32 dtype label
(DType has no I32).
- Drop unused imports/locals and the cuBLASLt scale-mode constants
orphaned by the strided-batched FP8 rework (e631a71).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
gpt-oss has no single-GPU engine path, so --tp 1 fell through to the
Qwen3-only engine and every request 503'd. Route gpt_oss to run_tp
even at tp=1: NCCL world-1 init works and all_reduce already no-ops
(bench-gpt-oss --tp 1 exercised this path). Quantized gpt-oss (22 GB
FP8 / 13 GB MXFP4) now serves on one 32 GB 5090.
Also fix eval_gsm8k_fast.py --gpu to accept a device list ("2,3"):
it was type=int, so any --tp 2 run pinned CUDA_VISIBLE_DEVICES to one
GPU and rank 1's set_device panicked while rank 0 spun in NCCL init.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Load the model's chat_template.jinja (or tokenizer_config.json
chat_template field) at startup and render it with minijinja instead of
hardcoded per-model prompt builders.
Custom Jinja functions: strftime_now (date formatting), raise_exception
(template validation errors). Falls back to Qwen3 ChatML template if
no Jinja template is found.
Removes the hardcoded build_prompt_gpt_oss() — the model's own template
now drives prompt formatting, matching llama.cpp's behavior exactly.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The gpt-oss model requires a specific prompt format with <|start|>,
<|message|>, <|end|>, <|channel|> tokens. Without this, the model
produces degenerate output. Auto-detected via config.model_type.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
pp_engine::run_pp: stage-0 coordinator (scheduler/tokenizer/sampling +
stop logic) on the calling thread, worker stage threads for 1..P. Each
step the coordinator embeds + runs its layers, then the hidden state is
handed stage->stage over NCCL P2P; the last stage samples and returns
the token to stage 0 over an in-process channel. v1 is serial (one
request, one token/step) — correctness first; throughput via microbatch
overlap is future work.
main: wire --pp N (mutually exclusive with --tp).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
tp_engine: rank-0 coordinator owns the scheduler and broadcasts per-token
commands (Register/Prefill/Decode/Free) to worker rank threads; the sampled
token always comes from rank 0, so it's correct for greedy and stochastic
sampling. Serial single-request path (sufficient for the quality benchmark).
--tp N selects it; TP=1 keeps the existing single-GPU Engine unchanged.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Fixes the paged-KV OOM at large --max-seq-len and adds elastic memory:
- Size the GPU block pool to available VRAM (cudaMemGetInfo) instead of the
worst-case blocks_per_seq * max_batch * 2 reservation, which OOM'd at 8192.
- Scheduler tracks waiting/running/swapped sets: block-aware admission,
swap-in of resumable sequences when blocks free, and preemption of the
newest running sequence to host when the pool can't cover a decode step.
- --swap-space-gb (default 8) sizes the pinned host swap pool;
XSERV_MAX_KV_BLOCKS forces a small pool to exercise swapping.
- api: poison-tolerant lock + clean 503 when the engine thread is gone,
instead of cascading mutex-poison panics.
Verified on RTX 5090: serves at --max-seq-len 8192 (previously OOM), and a
forced 40-block pool drives 48 lossless swap-out/swap-in cycles under
concurrency with coherent output.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New crate: xserv-server
- Engine thread: loads Qwen3-8B, processes requests sequentially
- axum HTTP server: /health, /v1/models, /v1/chat/completions
- tokio::sync::mpsc channel between API and engine threads
- Non-streaming JSON response (streaming SSE to be added later)
API is OpenAI-compatible:
POST /v1/chat/completions {"messages": [...], "max_tokens": N}
→ {"choices": [{"message": {"content": "..."}}]}
Verified: "Hi" → ", I'm" (3 tokens), model runs correctly via HTTP.
Key learnings:
- std::sync::mpsc::SyncSender is Send but NOT Sync → wrap in Mutex for Arc<AppState>
- MutexGuard must not live across await points (scope carefully)
- axum 0.8 Extension<Arc<T>> requires T: Send + Sync
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
