Single-head attention pool over Mamba2 K-positions, designed to replace
the CfC's zero-initialised `h_old` at k=0 with a learned content-
addressable summary over all K LN_b output positions. Forward math:
scores[k] = Q · keys[b, k, :] # [K]
attn[k] = softmax_k(scores) # [K]
context[h] = sum_k attn[k] * values[b, k, h] # [HIDDEN_DIM]
For our attention pool, keys == values == LN_b output [B, K, HIDDEN_DIM].
Single learned param: Q [HIDDEN_DIM]. Tiny (128 params).
Forward layout: grid = (B, 1, 1), block = HIDDEN_DIM=128 threads. Three
passes: (1) K dot-products with tree-reduce over HIDDEN_DIM, (2)
softmax over K with max-subtract+sum, (3) weighted sum into context.
Backward chain rule:
d_attn[k] = sum_h grad_context[h] * values[b, k, h]
d_scores[k] = attn[k] * (d_attn[k] - sum_kp attn[kp] * d_attn[kp])
d_Q[h] += sum_{b, k} d_scores[k] * values[b, k, h]
d_values[b, k, h] += grad_context[h] * attn[k] + d_scores[k] * Q[h]
Both `d_Q` and `d_values` use += semantics:
- d_Q: accumulates across batch (single block, internal n_batch loop).
- d_values: writes ADD onto whatever grad_ln_out already holds, so the
trainer can chain it on top of the K-loop's contribution to the LN_b
output gradient (no separate add-kernel needed).
Single-writer (no atomicAdd): one block per launch, thread h owns
column h of grad_ln_out for ALL (b, k). Internal n_batch loop matches
the GRN / VSN bwd pattern.
build.rs:
- "attention_pool" added to KERNELS
- Cache bust → v10
Wiring into PerceptionTrainer (Phase 3.2) is the follow-up commit:
add attn_q_d learned param + per-batch context + attn_weights buffers,
run attn_pool_fwd between LN_b fwd and the K-loop, use attn_context as
the K-loop's k=0 h_old (instead of zero_h_d), and chain attn_pool_bwd
after the K-loop reverse pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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