variable_selection_bwd refactored from grid=(1,1,1) to grid=(B*K,1,1).
VSN's n_rows = B*K positions (one row per (batch, K-position) pair);
block-per-row matches the existing fwd kernel's layout.
Adds 2 per-row grad scratch buffers + 2 reduce_axis0 launches:
vsn_grad_w_scratch_d [B*K, FEATURE_DIM, FEATURE_DIM]
vsn_grad_b_scratch_d [B*K, FEATURE_DIM]
~210 KB scratch at B=32, K=64.
VSN bwd runs 1×/step (not K×) so the absolute wall-time win here is
small versus commits 1+2. Done for pattern uniformity — every per-batch
or per-row bwd in the trainer now uses scratch+reducer.
All 9 perception_overfit smokes pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-position softmax-normalised feature gating for the trunk entry.
Per (b, k) sample:
gate_logit[i] = sum_j W_vsn[i, j] * x[j] + b_vsn[i]
gates = softmax(gate_logit) # [FEATURE_DIM]
y[i] = x[i] * gates[i]
Backward chain rule (cleanly factored from the softmax Jacobian):
d_gates[i] = grad_y[i] * x[i]
d_logit[i] = gates[i] * (d_gates[i] - sum_j gates[j] * d_gates[j])
grad_W[i,j] += d_logit[i] * x[j]
grad_b[i] += d_logit[i]
grad_x[j] = grad_y[j] * gates[j] + sum_i d_logit[i] * W[i,j]
Single-writer (no atomicAdd): thread tid owns row tid of grad_W and
column tid of d_x_via_W. ONE block per launch (loops n_rows internally),
same pattern as 2-layer / GRN bwd kernels.
Softmax uses standard max-subtract + sum trick for numerical
stability. Block dim = 64 (one warp + 24 idle threads at
FEATURE_DIM=40).
Wiring blocked on: Mamba2 backward needs to emit `d_input` (currently
dropped at line 1413 of mamba2_block.rs via `_d_input`). Next commit
exposes that so VSN bwd has the right grad_y signal — and the same
refactor unblocks Phase 2B (2-stack Mamba2 needs the inter-stack LN
to backprop through the 2nd stack's d_input).
build.rs:
- "variable_selection" added to KERNELS
- Cache bust → v9
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
