V1 paged_attention (decode ≤ 8192):
Fix: head_mapping int→Tensor conversion.
VERIFIED: matches manual attention, max diff < 0.001.
Perf: 0.034ms (256 tok), 0.059ms (1K), 0.169ms (4K), 0.272ms (8K).
V2 paged_attention (decode > 8192):
Native V2 kernel EXISTS (ixf_F.vllm_single_query_cached_kv_attention_v2)
but produces INCORRECT output (diff=1.28 vs V1 on same data).
Using Python V2 fallback (paged_attention_v2_pytorch.py) for now.
The native V2 expects [B,H,bs,d] layout (confirmed) but the output
values don't match even with correct layout conversion.
Prefill (flash_attn_func):
VERIFIED: ixf_F.flash_attn_func(q, k, v, causal=True) works
with head_dim=256 and GQA (num_kv_heads=4).
Patched into xformers.py as first-attempt before _run_sdpa_fallback.
Triton: symlinked /usr/local/lib/ → /usr/local/corex/lib64/ for import.
Hardware testing confirmed:
V1: K=[blocks, kv_heads, head_dim/x, block_size, x] (5D), V=[blocks, kv_heads, head_dim, block_size] (4D) → OK
V2: K=[blocks, kv_heads, block_size, head_dim] (4D), V=[blocks, kv_heads, block_size, head_dim] (4D) → OK
V2 with V1's layout → FAIL (Expected key_cache.dim()==4, value_cache.size(3)==head_size)
V1 and V2 use DIFFERENT cache memory layouts in ixformer.
V2 patch now converts cache on the fly before calling native kernel:
K: permute(0,1,3,2,4).reshape → [B,H,bs,d]
V: permute(0,1,3,2).contiguous → [B,H,bs,d]
This is a view+reshape for K (no copy if contiguous) and a transpose+contiguous for V.
The cost is one V copy per decode step, but this enables the native compiled V2 kernel
which is 10-100x faster than the Python fallback it replaces.
