4088f49b7d
cuda: infrastructure for whole-step CUDA graph capture
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- Thread-local launch stream (xserv_cuda::stream): every kernel
wrapper, cublasSetStream, and NCCL collective now launches on
current_stream_raw() — the legacy null stream by default (behavior
unchanged), or the capture stream installed via push_stream during
graph capture. Capture is impossible on the legacy stream.
- Allocator retain mode: blocks freed inside a retain window are
quarantined (RetainedBlocks) instead of pooled, so an instantiated
graph keeps exclusive ownership of every intermediate buffer it
references across replays.
- Capture mode GLOBAL -> THREAD_LOCAL: concurrent TP rank threads
must not poison each other's captures with their own cudaMallocs.
- embedding_device_ids / rope_inplace_device_pos: variants reading
token ids / positions from persistent device buffers, replacing the
per-call host upload that a captured region cannot contain.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com >
2026-06-12 20:12:37 +08:00
d5532ef209
phase 15: Tensor::empty + CUDA Graph infra — 50.3 tok/s (140% of HF, 45% roofline)
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Two optimizations:
1. Tensor::empty() — skip cudaMemset for output tensors
All kernel wrappers that fully overwrite their output now use
Tensor::empty() instead of Tensor::zeros(). Eliminates ~756
cudaMemset calls per decode step (21 per layer × 36 layers).
Improvement: 46.6 → 50.3 tok/s (+8%).
2. CUDA Graph infrastructure (for future use)
Added FFI bindings (cudaStreamBeginCapture, cudaGraphInstantiate,
cudaGraphLaunch) and RAII CudaGraph wrapper. Not yet used in the
forward pass due to variable kv_len, but provides foundation for
future graph-based decode optimization.
Ablation (dash5, RTX 5090, Qwen3-8B BF16, serial decode):
| Optimization | tok/s | vs HF | Roofline |
|-------------|-------|-------|----------|
| Phase 14 baseline | 12.9 | 36% | 12% |
| + Fused kernels | 13.2 | 37% | 12% |
| + Batched decode | 13.2 (serial) | 37% | 12% |
| + Custom GEMV | 46.6 | 130% | 42% |
| + Tensor::empty | 50.3 | 140% | 45% |
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-22 23:57:34 +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
2be27d6d94
perf: GPU transpose/reshape/repeat_kv kernels (eliminate CPU round-trips)
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New CUDA kernels (csrc/embedding/transpose.cu):
- reshape_heads_bf16: [S, H*D] → [1, H, S, D]
- merge_heads_bf16: [1, H, S, D] → [S, H*D]
- transpose_hsd_to_shd_bf16: [1, H, S, D] → [S, H, D] (for RoPE)
- transpose_shd_to_hsd_bf16: [S, H, D] → [1, H, S, D] (from RoPE)
- repeat_kv_bf16: [1, KV_H, S, D] → [1, KV_H*n_rep, S, D]
Rust wrappers (xserv-kernels/src/transpose.rs):
- reshape_heads_gpu, merge_heads_gpu, transpose_for/from_rope_gpu, repeat_kv_gpu
Qwen3 forward_gpu_cache now uses all GPU kernels — zero CPU data round-trips.
Result: 50/50 self-consistent, 3-5% faster (TBT 142→137ms)
Remaining bottleneck: ~900 device::synchronize() calls + 252 cuBLAS handle
creations per token (Phase 15 targets)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-05-22 12:01:07 +08:00
