Add a new FP8 quantization scheme (PER_TOKEN_HEAD, enum value 5) for the
batch_prefill FMHA kernel. Unlike PERTENSOR (single scale for all of Q/K/V)
or KV_BLOCKSCALE (per-page K/V scales), PER_TOKEN_HEAD applies fine-grained
descales:

  - Q descale: per-token, per-head  [total_q, nhead_q]
  - K descale: per-token, per-head  [num_total_pages, page_block_size, nhead_k]
  - V descale: per-head             [nhead_k]

The dequantization of the QK dot product is staged through LDS to avoid
inflating the inner-loop instruction footprint. Cross-page tiles
(page_block_size < kN0) are supported via per-column physical page lookup,
unlike KV_BLOCKSCALE which requires page_block_size >= kN0.

Additionally, an optional per-q-head P-scale [num_head_q] is supported.
The kernel folds log2(p_scale) into the exp2 row-max shift, so the scale
factor appears in both P and the rowsum l, cancelling in O = sum(P*V) / l
with no separate V-descale fixup needed.

Also adds page_size=64 to the codegen page size list, and includes SRD
same-page-skip optimizations for K/V window rebasing.

Changes:
  - block_attention_quant_scale_enum.hpp: PER_TOKEN_HEAD = 5
  - quant.hpp: enum, serialize ("pth"), decode
  - cpp_symbol_map.py: codegen symbol mappings
  - fmha_batch_prefill.py: page_size=64, per_token_head qscale, filter update
  - fmha_fwd.hpp: args struct (stride fields, p_scale_ptr), kargs forwarding
  - fmha_batch_prefill_kernel.hpp: kargs struct, MakeKargs, get_scale_s,
    pipeline dispatch
  - block_fmha_batch_prefill_pipeline_qr_ks_vs_async.hpp: LDS-staged dequant,
    p_scale_log2 exp2-shift fold, cross-page support, SRD same-page skip,
    PER_TOKEN_HEAD convenience overload

New decode-aligned KV cache layout for FP8 PER_TOKEN_HEAD batch_prefill:
5D vectorized K + 4D ColumnMajor V [NumBlocks, NumHeads, HeadDim, PageSize].
Matches the layout produced by reshape_and_cache and consumed by the decode
paged-attention kernel, so prefill can ingest the live KV cache without an
intermediate reshape.

- block_attention_kvcache_layout_enum.hpp: add VEC_K_COL_V_LAYOUT (= 2).
- fmha_batch_prefill_kernel.hpp: route K dram through the vectorized branch
  for VEC_K_COL_V; add a new V dram branch building (Pages, HeadDim,
  PageSize) with stride (batch_stride_v, page_block_size, 1) and merging to
  logical (D, TotalSeqK).
- block_fmha_batch_prefill_pipeline_qr_ks_vs_async.hpp: keep kAlignmentV =
  kMaxVecLoad for VEC_K_COL_V despite kPadSeqLenK=true (full-page invariant
  keeps vec loads safe along the contiguous PageSize dim).
- block_fmha_batch_prefill_pipeline_qr_ks_vs_async_default_policy.hpp: new
  kUseVectorizedVPolicy<Problem>() predicate routes VEC_K_COL_V through
  the same V tile dist / LDS layout / SmemKPack / alignment as
  VECTORIZED_LAYOUT.
- block_fmha_pipeline_problem.hpp: relax IsVLayoutRowMajor static_assert to
  accept ColumnMajor V for VEC_K_COL_V; introduce kIsKVectorized predicate
  so the page_size=1 + K-vectorized rejection covers the new layout.
- tile_fmha_traits.hpp: extend the supported-layouts static_assert.
- fmha_fwd.hpp: add `bool is_v_rowmajor = true` to fmha_batch_prefill_args
  so the wrapper can flip it for VEC_K_COL_V.
- codegen/ops/fmha_batch_prefill.py: add SUPPORTED_KV_MEMORY_LAYOUT_FP8_PTH_EXTRA
  map entry and a gated emission loop for fp8bf16 PER_TOKEN_HEAD with
  vlayout="col" + kv_memory_layout="vec_k_col_v" across both lookup tables.
  Relax receipt 200 to allow vlayout="col" only when kv_memory_layout
  == "vec_k_col_v".