corridorkey-mrp-mlx/tests
cmoyates cc2d31b435
feat(phase4d): shape + parity tests, fix attention transpose
Fix MaskUnitAttention output transpose (0,2,3,1,4 → 0,3,2,1,4) to
match PyTorch transpose(1,3) token ordering for windowed attention.

Parity results (4 stages):
  Stage 0: max_abs 2.9e-4, mean 1.6e-5
  Stage 1: max_abs 1.3e-4, mean 1.3e-5
  Stage 2: max_abs 1.1e-2, mean 5.2e-5 (16 blocks, expected drift)
  Stage 3: max_abs 5.8e-4, mean 2.6e-5

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-01 05:51:36 -03:30
..
__init__.py chore: add remaining test stubs and script placeholders 2026-03-01 04:49:09 -03:30
test_conversion.py feat(phase3): PyTorch→MLX weight converter (#1) 2026-03-01 05:16:56 -03:30
test_decoder_parity.py feat(phase2): MLX decoder + refiner with parity tests 2026-03-01 05:00:00 -03:30
test_hiera_stage_parity.py feat(phase4d): shape + parity tests, fix attention transpose 2026-03-01 05:51:36 -03:30
test_hiera_stage_shapes.py feat(phase4d): shape + parity tests, fix attention transpose 2026-03-01 05:51:36 -03:30
test_import.py chore: add remaining test stubs and script placeholders 2026-03-01 04:49:09 -03:30
test_reference_fixtures.py feat(phase1): PyTorch reference harness + fixture tests 2026-03-01 04:49:01 -03:30
test_refiner_parity.py feat(phase2): MLX decoder + refiner with parity tests 2026-03-01 05:00:00 -03:30