Feature/prefill fmha esimd#425
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Implements Flash Attention for prefill with paged KV cache, GQA, and causal mask using ESIMD intrinsics. All 8 UT cases pass with max diff < 0.002 vs IPEX reference. Current performance: ~23x slower than IPEX (JIT mode, kBr=4, kBc=16, scalar dot product). Hardware metrics show 0% XMX utilization — next step is adding DPAS for Q×K^T score computation. Key design decisions: - JIT compilation (-fsycl-targets=spir64) to avoid AOT GRF overflow - kBr=4 Q rows per work group with shared K/V loading - kBc=16 batch softmax with safe_exp16 (clamp + merge to avoid NaN) - head_dim=256 hardcoded for Qwen3.5 series models - Supports: causal mask, paged KV (block_table), GQA (any ratio) Build: TORCH_XPU_ARCH_LIST=bmg-g21 MAX_JOBS=8 python3 setup.py bdist_wheel Test: ZE_AFFINITY_MASK=4 python tests/test_prefill_fmha.py 1 2 3 4 5 6 7 8 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Implements split-KV architecture: - Sub-kernel: kBr=4 per-token online softmax, processes PARTITION_SIZE tokens of KV range, outputs partial_out + row_max + row_sum (float32) - Reduce kernel: log-sum-exp merge across partitions → final fp16 output - Host launcher: dispatches all partitions in parallel, then reduce Performance: 15.1x slower than IPEX (from 18x without split-KV). Limited improvement because GPU EUs are already saturated with WGs. Next step: add DPAS to sub-kernel for per-WG compute acceleration. All 8 UT cases pass (max diff < 0.002). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ction Current state: - kBr=8, kBc=16, DPAS score, SIMD exp, scalar max/sum - JIT mode (-fsycl-targets=spir64) - All 8 UT cases pass (max diff < 0.002) - Performance: 93x (slow due to large kernel body from select ops) Known JIT bugs: - reduce<float>(simd, maximum<>()) returns wrong value (simd[0] not max) - pointer array simd*[N] indirect access produces NaN Next: try manual tree reduction for max to avoid both reduce bug and select overhead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Documents all findings, JIT bugs, performance analysis, and next steps. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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