Shape-spy on a real 2048 forward + exact-shape sweeps on M1/M3 Ultra:
fused==unfused at hdim 56 (silent fallback) on both machines, so use_sdpa
buys nothing and its transpose choreography costs ~4.5x on M1. Padding to
hdim 64 engages the fast kernel: 2.2x (M3) / 2.7x (M1) on the dominant
global-attention shape despite 14% extra FLOPs.
Ran scripts/bench_optimizations.py unmodified across three machines + an
engine-level tiled×compile extension with ground-truth alpha scoring.
Headlines: sdpa = 4.5x regression on M1 at 2048 (bf16 innocent — isolated
per-toggle on both Ultras); stage_gc harmful on Ultra-class; tiled 768/64
+ compile = best 2048 config on both Ultras; 24GB Macs swap-bound at 2048
full-frame (tiled required).
Ablation sweep across 512/1024/2048 resolutions with 8 configs.
Tiled 768/64 = 1.5x faster + 11.4x less memory vs full-frame at 2048.
stage_gc net negative at small res, breaks even at 2048.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
load_model() now defaults to dtype=bf16, fused_decode=True. Both are
free (zero parity regression, bit-exact fused path). Backbone/sigmoid
stay fp32.
Plan updated with benchmark results: tiled+GC = 12x peak memory
reduction at 2048x2048 (27.6GB → 2.3GB), all acceptance criteria met.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Script + pytest tests to verify MLX inference works at CorridorKey's
training resolution. Uses samples/ by default, synthetic fallback.
Reports timing, peak memory, output diagnostics.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Drop-in MLX backend for main CorridorKey repo. Engine class wraps
existing model/inference with process_frame() API returning alpha,
fg, comp, processed as uint8 numpy arrays. Lowers Python to >=3.11,
removes unused deps, adds smoke script and 16 contract tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cache nn.Upsample instances in DecoderHead/GreenFormer __init__
(eliminated ~7 allocations per forward pass)
- Add mx.compile() support via load_model(compile=True)
- Benchmark harness: eager vs compiled, multi-resolution, parity checks
- Tiled inference with overlap blending for large images
- Profiling utilities with forced mx.eval for accurate timing
- Reference comparison script (scripts/compare_reference.py)
- 12 new tests (compiled consistency + tiling)
- README performance section with Apple Silicon guidance
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire GreenFormer (backbone + decoders + refiner), add image I/O,
inference pipeline, CLI entry point, and 3 test files (23 new tests).
E2e parity vs PyTorch: max abs err < 1.5e-4 across all outputs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
