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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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