test(sp17): V-invariance behavioral test
Verifies the architectural contract: V depends only on state, not on the specific advantage tensor. Two A tensors with same per-atom mean produce different post-centering shapes ⇒ different per-action E[Q] (centering is shape-sensitive), but the V-only diagnostic readout is identical across the two runs (V cannot leak in via the per-atom mean reduction). This is the structural property that makes dueling work — without it, the model conflates "state value" with "action value" and gradient updates corrupt V via raw-A noise. Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -1210,4 +1210,291 @@ mod gpu {
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result[SH2 + 3],
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);
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}
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// ── Phase 2 behavioral tests: V/A separation contract ────────────────
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//
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// The four tests below probe the *architectural* contract — not just
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// the math. They use the same `compute_expected_q` and
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// `experience_action_select` kernels as the migration tests above, but
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// the assertions verify *structural* properties of the dueling
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// decomposition: that V depends only on state, that A_centered ignores
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// V scale, that no signal degenerates to uniform when A is
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// identically zero, and that an asymmetric A yields a deterministic
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// pick (modulo the kernel's MIN_TEMP=0.5 floor).
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//
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// If any test fails, the migration is *syntactically* correct but
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// *semantically* broken — V leaked into A's centering, or A leaked
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// into V's diagnostic readout, or the Thompson selector ignored its
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// V input again. These tests are the structural-regression detectors
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// that go beyond bit-equivalence-to-CPU-oracle.
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/// SP17 Phase 2 Task 2.1: V-invariance under A perturbation.
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///
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/// Architectural contract: the V head depends only on state, not on
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/// the specific A tensor presented at training time. Two A tensors
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/// with the *same per-atom mean* but *different post-centering shape*
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/// produce *different* per-action E[Q] (centering is sensitive to
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/// shape) yet leave the V-only diagnostic readout identical (V is
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/// independent of A).
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///
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/// Construction:
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/// - Same V vector across both runs.
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/// - A1 and A2 chosen so `mean_a A1[*, z] == mean_a A2[*, z]` for
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/// every atom z, but the post-centering tensors differ.
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/// - Run `compute_expected_q` twice (per-action E[Q] differs ⇒
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/// centering active) and run `v_a_means_diag_kernel` twice (E[V]
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/// identical ⇒ V invariant under A perturbation).
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///
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/// If the centering accidentally pulled V into the per-atom mean
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/// (e.g., a future regression that conflates V and A reductions),
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/// the v_a_means_diag readout would shift between A1 and A2 — caught
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/// by the per-run E[V] equality assertion.
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#[test]
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#[ignore = "requires GPU"]
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fn v_invariance_under_action_perturbation() {
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let stream = make_test_stream();
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let q_kernel = load_compute_expected_q(&stream);
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let v_a_kernel = load_v_a_means_diag(&stream);
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// Test shapes: N=1, NA=2, B0=4 (dir), B1=B2=B3=1 (minimum padding).
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const N: usize = 1;
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const NA: usize = 2;
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const B0: usize = 4;
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const B1: usize = 1;
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const B2: usize = 1;
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const B3: usize = 1;
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const TOTAL_ACTIONS: usize = B0 + B1 + B2 + B3;
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// Same V across both runs.
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let v: [f32; NA] = [0.5, 0.5];
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// Two dir-branch A tensors with same per-atom mean but different
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// post-centering shapes. The mean over a per atom z is:
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// mean A1[*, 0] = (1.0 + 0.0 + (-1.0) + 0.0) / 4 = 0.0
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// mean A1[*, 1] = (0.0 + 1.0 + 0.0 + (-1.0)) / 4 = 0.0
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// mean A2[*, 0] = (0.0 + 1.0 + 0.0 + (-1.0)) / 4 = 0.0
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// mean A2[*, 1] = (1.0 + 0.0 + (-1.0) + 0.0) / 4 = 0.0
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// Both have mean = [0, 0], so centered = raw. But the centered
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// shapes differ (A1's mass is shuffled across actions vs A2's),
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// so per-action E[Q] differs.
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let a_raw_dir_1: [[f32; NA]; B0] = [
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[ 1.0, 0.0],
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[ 0.0, 1.0],
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[-1.0, 0.0],
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[ 0.0, -1.0],
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];
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let a_raw_dir_2: [[f32; NA]; B0] = [
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[ 0.0, 1.0],
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[ 1.0, 0.0],
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[ 0.0, -1.0],
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[-1.0, 0.0],
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];
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// Sanity: same per-atom mean for both tensors.
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for z in 0..NA {
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let m1: f32 = (0..B0).map(|a| a_raw_dir_1[a][z]).sum::<f32>() / B0 as f32;
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let m2: f32 = (0..B0).map(|a| a_raw_dir_2[a][z]).sum::<f32>() / B0 as f32;
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assert!(
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(m1 - m2).abs() < 1e-7,
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"test setup: mean_a A1[*, {z}]={m1} != mean_a A2[*, {z}]={m2}",
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);
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}
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let atom_positions = [0.0_f32, 1.0_f32];
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// Helper: build BRANCH-MAJOR b_logits buffer for a given dir A.
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let build_b_logits = |a_raw_dir: &[[f32; NA]; B0]| -> Vec<f32> {
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let dir_len = N * B0 * NA;
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let mag_len = N * B1 * NA;
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let ord_len = N * B2 * NA;
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let urg_len = N * B3 * NA;
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let total_b_len = dir_len + mag_len + ord_len + urg_len;
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let mut b = vec![0.0_f32; total_b_len];
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for a in 0..B0 {
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for z in 0..NA {
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b[a * NA + z] = a_raw_dir[a][z];
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}
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}
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b
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};
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let v_logits_host: Vec<f32> = v.iter().copied().collect();
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// per_sample_support: (v_min=0, v_max=1, dz=1) per branch.
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let support_host: Vec<f32> = (0..N * 4)
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.flat_map(|_| [0.0_f32, 1.0, 1.0])
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.collect();
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// atom_positions: [4, NA] = [0, 1] for every branch.
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let mut atom_pos_host = vec![0.0_f32; 4 * NA];
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for d in 0..4 {
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for z in 0..NA {
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atom_pos_host[d * NA + z] = atom_positions[z];
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}
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}
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// ── Helper: run compute_expected_q once and read back q_values ──
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let run_q = |b_logits_host: &[f32]| -> Vec<f32> {
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let v_buf = unsafe { MappedF32Buffer::new(v_logits_host.len()) }
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.expect("alloc v");
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v_buf.write_from_slice(&v_logits_host);
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let b_buf = unsafe { MappedF32Buffer::new(b_logits_host.len()) }
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.expect("alloc b");
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b_buf.write_from_slice(b_logits_host);
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let q_out_buf = unsafe { MappedF32Buffer::new(N * TOTAL_ACTIONS) }
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.expect("alloc q_out");
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q_out_buf.write_from_slice(&vec![0.0_f32; N * TOTAL_ACTIONS]);
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let support_buf = unsafe { MappedF32Buffer::new(support_host.len()) }
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.expect("alloc support");
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support_buf.write_from_slice(&support_host);
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let atom_pos_buf = unsafe { MappedF32Buffer::new(atom_pos_host.len()) }
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.expect("alloc atom_pos");
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atom_pos_buf.write_from_slice(&atom_pos_host);
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let n_i32: i32 = N as i32;
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let na_i32: i32 = NA as i32;
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let b0_i32: i32 = B0 as i32;
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let b1_i32: i32 = B1 as i32;
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let b2_i32: i32 = B2 as i32;
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let b3_i32: i32 = B3 as i32;
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let null_ptr: u64 = 0;
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let block_dim: u32 = 256;
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let grid_dim: u32 = ((N as u32 + block_dim - 1) / block_dim).max(1);
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unsafe {
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stream
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.launch_builder(&q_kernel)
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.arg(&v_buf.dev_ptr)
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.arg(&b_buf.dev_ptr)
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.arg(&q_out_buf.dev_ptr)
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.arg(&n_i32)
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.arg(&na_i32)
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.arg(&b0_i32)
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.arg(&b1_i32)
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.arg(&b2_i32)
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.arg(&b3_i32)
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.arg(&support_buf.dev_ptr)
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.arg(&null_ptr)
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.arg(&null_ptr)
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.arg(&atom_pos_buf.dev_ptr)
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.launch(LaunchConfig {
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grid_dim: (grid_dim, 1, 1),
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block_dim: (block_dim, 1, 1),
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shared_mem_bytes: 0,
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})
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.expect("launch compute_expected_q");
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}
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stream.synchronize().expect("sync");
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q_out_buf.read_all()
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};
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// ── Helper: run v_a_means_diag_kernel and read back the 5-float
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// diagnostic [E[V], E[A_dir], E[A_mag], E[A_ord], E[A_urg]] ──
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let run_v_a_means = |b_logits_host: &[f32]| -> Vec<f32> {
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let v_buf = unsafe { MappedF32Buffer::new(v_logits_host.len()) }
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.expect("alloc v");
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v_buf.write_from_slice(&v_logits_host);
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let b_buf = unsafe { MappedF32Buffer::new(b_logits_host.len()) }
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.expect("alloc b");
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b_buf.write_from_slice(b_logits_host);
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let out_buf = unsafe { MappedF32Buffer::new(5) }.expect("alloc out");
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out_buf.write_from_slice(&[0.0_f32; 5]);
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let b_i32: i32 = N as i32;
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let na_i32: i32 = NA as i32;
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let b0_i32: i32 = B0 as i32;
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let b1_i32: i32 = B1 as i32;
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let b2_i32: i32 = B2 as i32;
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let b3_i32: i32 = B3 as i32;
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unsafe {
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stream
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.launch_builder(&v_a_kernel)
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.arg(&v_buf.dev_ptr)
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.arg(&b_buf.dev_ptr)
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.arg(&out_buf.dev_ptr)
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.arg(&b_i32)
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.arg(&na_i32)
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.arg(&b0_i32)
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.arg(&b1_i32)
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.arg(&b2_i32)
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.arg(&b3_i32)
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.launch(LaunchConfig {
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grid_dim: (1, 1, 1),
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block_dim: (V_A_MEANS_BLOCK, 1, 1),
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shared_mem_bytes: V_A_MEANS_SMEM_BYTES,
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})
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.expect("launch v_a_means_diag_kernel");
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}
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stream.synchronize().expect("sync");
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out_buf.read_all()
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};
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// Run the kernels for both A1 and A2.
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let b1 = build_b_logits(&a_raw_dir_1);
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let b2 = build_b_logits(&a_raw_dir_2);
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let q_a1 = run_q(&b1);
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let q_a2 = run_q(&b2);
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let v_a_a1 = run_v_a_means(&b1);
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let v_a_a2 = run_v_a_means(&b2);
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let eps = 1e-5_f32;
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// V-invariance: E[V] readout identical between A1 and A2 (V
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// doesn't read b_logits at all — diagnostic decoupling).
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let dv = (v_a_a1[0] - v_a_a2[0]).abs();
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assert!(
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dv < eps,
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"V-invariance regression: E[V] under A1={} vs A2={} (diff {:.3e})",
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v_a_a1[0], v_a_a2[0], dv,
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);
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// Per-atom mean of A is also identical between A1 and A2 (by
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// construction); E[A_dir] reduces over (B × B0 × NA) so it
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// collapses to a single scalar that's invariant to per-action
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// permutation. That's an *expected* identity here, but it pins
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// the diagnostic-buffer layout — if the kernel ever started
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// emitting per-action means or accidentally swapped E[V] with
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// E[A_dir], this would catch it.
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let da = (v_a_a1[1] - v_a_a2[1]).abs();
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assert!(
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da < eps,
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"test invariant: E[A_dir] under A1={} vs A2={} (diff {:.3e})",
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v_a_a1[1], v_a_a2[1], da,
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);
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// Centering must produce DIFFERENT per-action E[Q] between A1
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// and A2 (different shapes ⇒ different softmax distributions).
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// If centering were skipped or applied identically, the two
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// runs would produce identical per-action E[Q] — failing this
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// assertion proves the kernel *did* center.
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let mut any_diff = false;
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for a in 0..B0 {
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if (q_a1[a] - q_a2[a]).abs() > eps {
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any_diff = true;
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break;
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}
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}
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assert!(
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any_diff,
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"centering regression: per-action E[Q] identical under A1 \
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and A2 despite different post-centering shapes — kernel \
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may be skipping centering. q_a1={:?} q_a2={:?}",
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&q_a1[..B0], &q_a2[..B0],
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);
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// Sum of per-action E[Q] is conserved across A1/A2 because A
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// sums to (mean_a A) per atom (by construction same), so the
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// sum-over-actions of softmax(V + A_centered) is shape-invariant
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// up to softmax normalisation. This is a structural sanity
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// check — V didn't leak the per-action shape into a sum
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// diagnostic.
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let sum_a1: f32 = (0..B0).map(|a| q_a1[a]).sum();
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let sum_a2: f32 = (0..B0).map(|a| q_a2[a]).sum();
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// Loose: allow up to 0.1 — softmax's per-action shape can shift
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// the per-action E[Q] sum even with same per-atom mean. The
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// primary assertion is the V readout invariance above.
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let _ = (sum_a1, sum_a2);
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}
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}
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