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a67e724119
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docs: Phase 15 design doc + benchmark report
Design document (docs/15-performance.md):
- Roofline analysis: 112 tok/s theoretical at 1.79 TB/s
- Bottleneck quantification: cuBLAS M=1 GEMV at 8% bandwidth → 77% of step time
- Six optimizations with rationale, implementation details, and expected impact
- Ablation table with per-optimization delta measurements
- Remaining 55% roofline gap breakdown with next-step priorities
Benchmark report (docs/benchmarks/phase15-performance.md):
- Full ablation: 12.9 → 50.3 tok/s across 6 optimizations
- Per-prompt detail (8 prompts, 46-51 tok/s range)
- Concurrent throughput analysis (batch=4 vs serial)
- Phase-over-phase tracking from Phase 8 to Phase 15 (2.5 → 50.3 tok/s)
- Correctness verification (9/10 top-1 match, 52/52 API pass)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-23 00:39:27 +08:00 |
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6cc1c9332d
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docs: Phase 14 design doc + benchmark, fix Phase 11/12 honesty
Phase 14 (Flash Attention):
- Design doc: FA2 algorithm, SM120 hardware constraints (FA4 incompatible),
kernel config (BR=BC=64, 32KB smem), GQA mapping, causal tile-skip,
known limitations and optimization roadmap
- Benchmark doc: correctness (9/10 top-1 match, identical to pre-FA baseline),
performance tracking (6.9→10.3→12.9 tok/s across phases), memory savings
analysis, remaining bottleneck breakdown
Phase 11 doc: title corrected from "Paged Attention" to "GPU-Resident KV Cache"
with explicit note that paged allocation was not implemented.
Phase 12 doc: "当前状态" updated from "未实现" to reflect actual state —
iteration-level scheduling implemented + verified (6.0x concurrent speedup),
batched GPU forward explicitly marked as not yet implemented.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-22 18:51:29 +08:00 |
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268e40d764
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phase 10: add Qwen3-8B benchmark + performance fix
Benchmark infrastructure:
- bench-qwen3 binary: 50 prompts × 20 tokens with KV cache
- bench_compare_qwen3.py: comparison against HF transformers (BF16)
Performance fix:
- Precompute transposed weights at model load time (eliminated per-token
weight transpose CPU round-trip: was 252 transposes × 32MB each = 8GB/token)
- Result: from "infinite" (>10 min/token) to 144ms/token
Results (50 prompts):
- Prefill top-1: 42/50 (84%), top-5: 50/50 (100%) vs HF transformers
- Greedy sequence: 0/50 exact match (BF16 precision drift over 36 layers)
- Performance: TTFT=138ms, TBT=144ms, 6.9 tok/s (HF: 21ms, 45.6 tok/s)
- All outputs are coherent English/Chinese
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-22 10:25:33 +08:00 |
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64084d3489
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phase 9: KV cache + autoregressive generation
- KVCache: per-layer, per-head storage with append + reconstruct
- forward_with_cache: prefill (full prompt) + decode (single token) modes
- Fixed data layout bug: per-head vectors avoid cross-head interleaving
- CLI updated to use KV cache by default
- bench-gpt2 supports --no-cache flag for comparison
Benchmark results (50 prompts × 20 tokens):
- KV cache vs no-cache: 50/50 bit-identical (cache is correct)
- 18x speedup: TTFT 400→24ms, TBT 407→22ms, throughput 2.5→44 tok/s
- vs HF transformers: 40/50 match (10 are FP divergence, avg logit gap 0.20)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-21 23:39:41 +08:00 |
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cb12250ef0
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phase 8: add benchmark framework + baseline results
- bench-gpt2 binary: runs 50 prompts, measures TTFT/TBT per prompt, outputs JSON
- bench_compare.py: compares xserv vs transformers token-by-token + timing
- Baseline results: 50/50 correctness, 400ms TTFT / 407ms TBT (100x slower than PyTorch)
- Bottlenecks documented: no KV cache, CPU round-trips, cuBLAS handle churn
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-05-21 23:29:41 +08:00 |
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