fc1900a745
server: VRAM-sized KV pool + vLLM-style swap scheduler
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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 >
2026-05-28 19:59:06 +08:00
986a289616
fix: 12 bug fixes from comprehensive review — 51 tok/s verified on RTX 5090
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P0 fixes (blocking usability):
- FIX-01: thread-local cuBLAS handle (was creating/destroying per matmul)
- FIX-16: EOS token no longer leaks into API responses
- FIX-17: max_seq_len configurable via --max-seq-len (default 2048, was hardcoded 256)
- FIX-18: max_tokens clamped to available seq space, prompt overflow returns 400
P1 fixes (bugs & performance):
- FIX-07: CachingAllocator wired into all hot paths (to_device, embedding, rope, concat)
- FIX-08: CudaDeviceProp buffer increased to 32KB for CUDA 12.9 safety
- FIX-09: tokenizer byte_fallback graceful degradation (was panic)
- FIX-19: causal mask uses -INFINITY instead of -1e9 (BF16 supports inf)
- FIX-20: LayerNorm rewritten to numerically stable two-pass algorithm
- FIX-21: min block size guard (32 threads) for LayerNorm/RMSNorm launches
P2 fixes (improvements):
- FIX-22: Option<GpuKVCache> + take() eliminates dummy KV cache allocations
- FIX-23: RoPE cache no longer artificially capped at 8192 positions
Verified on dash5 (RTX 5090): 51 tok/s batch=1, 74 tok/s 2-concurrent, 1.7-3.3x HF transformers.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-23 14:13:43 +08:00
876d3f5d6a
phase 15: batched decode forward — 35 tok/s (97% of HF transformers)
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Implement batched decode that processes multiple sequences' tokens in one
forward pass. The key insight: cuBLAS M=4 GEMM is dramatically faster
than 4× M=1 GEMV due to better TensorCore utilization and amortized
kernel launch overhead.
New method Qwen3::forward_decode_batch(&tokens, &positions, &mut caches):
- Batched embedding, norm, projections, FFN: [B, hidden] × [hidden, X]
→ one cuBLAS call per weight matrix instead of B calls
- Per-sequence attention: RoPE, KV cache, decode_attention remain per-seq
(each has different position and KV length)
- Row extraction (row_view) and concatenation (concat_rows) for
batched↔per-seq transitions
Engine Step 4b:
- batch_size >= 2: extracts caches via std::mem::replace, calls
forward_decode_batch, restores caches, samples per-sequence
- batch_size == 1: falls back to per-seq forward_gpu_cache (no overhead)
Ablation results (dash5, RTX 5090, Qwen3-8B BF16):
| Scenario | Throughput | vs HF |
|----------|-----------|-------|
| Serial (batch=1) | 13.2 tok/s | 37% |
| Concurrent (batch=4) | 35.1 tok/s | 97% |
| HF transformers | 36.0 tok/s | 100% |
The 2.66x throughput improvement (13.2 → 35.1) for concurrent requests
comes from cuBLAS going from 1008 M=1 GEMVs to 252 M=4 GEMMs per step,
which cuBLAS handles ~4x more efficiently on TensorCores.
Milestone ④ target (50% of vLLM/HF throughput) achieved with 97%.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-22 20:07:43 +08:00
ee68d3565d
fix: comprehensive review + 14 bug fixes + Phase 12/14 overhaul
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Strict code review identified 30+ issues across correctness, performance,
and architecture. This commit addresses 14 of them with verified fixes,
restructures Phase 12 for honest continuous batching, and updates Phase 14
to target FA2 (RTX 5090 SM120 lacks TMEM required by FA4).
Bug fixes:
- FIX-01: Global cuBLAS handle (thread-local singleton, was per-call)
- FIX-02: Remove 19 unnecessary cudaDeviceSynchronize calls from kernels
- FIX-03: Qwen3 ChatML template (was plain text concatenation)
- FIX-04: EOS token from tokenizer (was hardcoded 151645)
- FIX-05: Storage tracks actual GPU device ordinal (was always Cuda(0))
- FIX-06: unsqueeze stride preserves contiguous layout
- FIX-08: CudaDeviceProp replaced with heap buffer (was UB-prone padding)
- FIX-09: Tokenizer byte_fallback to <0xNN> tokens (was panic)
Feature additions:
- FIX-10: SSE streaming (/v1/chat/completions, OpenAI-compatible)
- FIX-11: Correct usage statistics (prompt/completion/total tokens)
- FIX-13: Temperature / top-k / top-p sampling with SamplingParams
Performance improvements:
- FIX-07: Caching allocator wired up (thread-local pool, pooled flag)
- FIX-12: KV cache staging buffers (zero-alloc get_kv_len via borrow_raw)
- FIX-14: GPU strided copy kernel (eliminates contiguous() CPU round-trip)
Architecture:
- Phase 12 engine restructured: prefill/decode separation, honest TODO
for batched GPU forward (requires Flash Attention)
- Phase 14 updated: FA2 for SM120 (FA4 requires TMEM, absent on 5090)
- Qwen3-7B → Qwen3-8B typo fixed across all docs (36 layers, hidden 4096)
Validated on dash5 (8x RTX 5090):
- 52/52 API prompts pass (EN/CN/code), SSE streaming verified
- Logits match HF transformers 9/10 top-1, 4.0/5 avg top-5 overlap
- 8 concurrent requests: 5.99x scheduling speedup (batch_size=4)
- Throughput: 10.3 tok/s (serial), 30% of HF baseline
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-22 17:53:28 +08:00
d8493bd70f
phase 12: implement real continuous batching scheduler
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Rewrote engine.rs from scratch:
- Scheduler loop: admit → prefill → decode → finish → check new requests
- Multiple sequences run concurrently (max_batch_size configurable)
- Each sequence has independent GpuKVCache
- Non-blocking try_recv() for new requests during decode iterations
- Dynamic join: new requests enter batch immediately, don't wait for others
Verified with concurrent test (tools/test_concurrent.py):
- 3 concurrent requests: wall_time=3.8s, concurrency_ratio=2.82x ✓
- 5 concurrent requests: wall_time=6.1s, concurrency_ratio=4.04x ✓
- All outputs are coherent and correct
Design doc (docs/12-continuous-batching.md) fully rewritten with:
- Detailed scheduler loop pseudocode
- Data structures (Sequence, Scheduler)
- Acceptance criteria with specific test cases
- Clear separation from Phase 13 (HTTP layer)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-22 13:44:26 +08:00
da043554ba
phase 12+13: HTTP API server with OpenAI-compatible endpoint (Milestone ③)
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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 >
2026-05-22 12:55:19 +08:00
