# SPDX-License-Identifier: AGPL-3.0-or-later
# Copyright (C) 2026 David (Corey) Herrera
# attention_float16b.cbtable
#   The CB descriptor contract the FLASH-DECODE ATTENTION compute kernel is compiled
#   against (resident_attention_compute.c). It generates ft_tensix_cb_formats.h and MUST
#   match, byte for byte, the L1 CB config array ft_tt_op_attention_place_cbs() writes at
#   launch. There is no runtime check (docs/tt_kernel_model.md §11).
#
# EVERY BUFFER IS Float16_b, AND THAT IS WHAT LICENSES THE ABSENT reconfig_data_format().
#   The kernel mixes a matmul (reversed source order) with row reduces, broadcast-column
#   multiplies and eltwise adds. Upstream's own sdpa kernel reconfigures the unpacker
#   between those phases -- because it carries operands of different formats. Here one
#   format serves every buffer, exactly as in reduce_float16b.cbtable, so the reconfig has
#   nothing to reprogram. If a shape ever scores wrong, that is the first line to add back.
#
# dst_accum_mode 1 IS REQUIRED HERE, NOT PREFERRED.
#   The running max, the running row sum and the output accumulator are all carried across
#   EVERY KV block, so a 16-bit destination accumulator would re-round all three once per
#   block -- docs/tt_bringup_log.md §19's failure mode multiplied by the block count (128
#   blocks at 32k context, against the 144 K-tiles that grew a matmul's error 159x). §19
#   also measured the cost of fp32 accumulate at zero wall clock on a data-movement-bound
#   op, which this is.
#
# math_fidelity 4 is HiFi4, recorded rather than inherited (§16). It is free here for the
#   same reason it is free on the matmul: one HiFi4 tile matmul is 64 cycles = 80 ns at
#   800 MHz, and the reader delivers a tile pair every ~104 ns.

math_fidelity  4
approx         0
dst_accum_mode 1

# in                                          index  format     tile geometry
cb  0  Float16_b 32 32 16 4   # cb_q        the packed query, as the reader built it
cb  1  Float16_b 32 32 16 4   # cb_k        one K block
cb  2  Float16_b 32 32 16 4   # cb_v        one V block
cb  3  Float16_b 32 32 16 4   # cb_const    scaler, 1/sqrt(d), tail mask, dead mask
cb 14  Float16_b 32 32 16 4   # cb_qs       the scaled query

# intermediates
cb  4  Float16_b 32 32 16 4   # cb_s        scores, then the exponentials in place
cb  5  Float16_b 32 32 16 4   # cb_scratch  the mask bounce
cb  6  Float16_b 32 32 16 4   # cb_m_a      running max, ping
cb  7  Float16_b 32 32 16 4   # cb_m_b      running max, pong
cb  8  Float16_b 32 32 16 4   # cb_l        running row sum
cb  9  Float16_b 32 32 16 4   # cb_lsum     this block's row sum
cb 10  Float16_b 32 32 16 4   # cb_c        exp(m_old - m_new), then 1/l
cb 11  Float16_b 32 32 16 4   # cb_o        the output accumulator
cb 12  Float16_b 32 32 16 4   # cb_pv       this block's P.V

# out
cb 16  Float16_b 32 32 16 4   # cb_out