Docs Native layer suite (Lucy menu [14])

Native layer suite (Lucy menu [14])

Run: Lucy Bloom Rivers → [14] (or [0] for all layer types).
Code: Lucy examples/seven_layer/native_menu.go
Runtime log: lucy_testing_output/native_layers.txt (reset each session)

This suite exercises native-exact training (UseExactDType = true): forward and backward in storage dtype via *_native.go, plus 30-epoch CPU training per dtype. When Plan 9 SIMD is linked, each row also reports native-exact SIMD fwd/bwd timing vs scalar native.

Contrast with menu [7] (seven-layer suite): default QAT-like path (GetActive FP32 dequant), SC/MC/SIMD parity, save/reload. See training.md — Training paradigms.

Harness shape

Setting Value
Grid 1³ (one cell)
Layers per cell 7 (same stack shape as other native suites; primary layer type under test)
Dtypes 21 (IsLayerNativeExactDType)
Train epochs 30
SIMD SetSimdForward(true) for timing columns; *_native_simd.go when linked

Layer types (menu [1]–[10]): Dense, SwiGLU, MHA, CNN1, CNN2, CNN3, RNN, LSTM, Embedding, Residual.

Per-row gates: Path (native routing) · Fwd · Bwd · Train (loss finite + harness trainingOK) · optional SIMD fwd/bwd speedup.

Full-run results (Jul 2026)

Captured logs:

Platform Log file
amd64 native_layers_amd.txt (user archive; same format as native_layers.txt)
arm64 native_layers_arm.txt

Pass summary (21 dtypes × layer)

Layer amd64 arm64 Notes
Dense 21/21 21/21
SwiGLU 21/21 21/21 Int8 learns on both (e.g. amd64 0.22→0.06)
MHA 21/21 21/21
CNN1 21/21 21/21
CNN2 21/21 21/21 Many dtypes flat loss (stable, not diverging)
CNN3 21/21 20/21 arm64 Int32 train: loss 0.29→0.36
RNN 20/21 19/21 amd64 Int2 train fail; arm64 Int4, Int2 train fail
LSTM 21/21 21/21
Embedding 21/21 21/21
Residual 21/21 21/21 No skip wire in 1³ forward chain; loss flat by design
Total 209/210 207/210 Fwd/bwd pass on all rows; train fails listed above

Train failures are loss-criteria only (forward/backward still PASS). Low-bit RNN at 30 epochs on a tiny 1³ stack is the flaky zone; wide unsigned integers (Uint64/Uint32) often start from huge loss then recover within 30 epochs.

Native-exact SIMD speedup (Float32, 1³)

Speedup = scalar native time ÷ native SIMD time (>1 = SIMD faster). Representative Float32 rows from the archived logs:

Forward (SIMD vs scalar native)

Layer amd64 arm64
Dense 1.6× 1.6×
SwiGLU 2.4× 3.8×
MHA 1.3× 1.5×
CNN1 19.7× 13.5×
CNN2 56.2× 65.5×
CNN3 33.0× 41.9×
RNN 17.1× 14.1×
LSTM 28.3× 17.9×
Embedding 1.5× 2.2×
Residual ≈1× n/a (sub-µs; skip not exercised)

MAC dtypes (Float16, FP8, Int32, …) often see 8–11× forward wins on amd64 where scalar native still does per-dot GetNative work and SIMD materializes f32 tiles once.

Backward (SIMD vs scalar native)

Layer amd64 arm64
Dense 8.5× 7.7×
SwiGLU 6.3× 3.7×
MHA 2.9× 2.6×
CNN1 8.0× 6.1×
CNN2 21.2× 26.0×
CNN3 17.3× 16.9×
RNN 9.0× 5.8×
LSTM 12.3× 11.8×
Embedding 7.4× 3.0×

True integer dtypes (Int8) — CNN backward

Int8 native backward uses SaxpyI8*; speedups vs scalar int8 backward are extreme on conv layers:

Layer amd64 bwd arm64 bwd
CNN1 Int8 35× n/a (timer resolution)
CNN2 Int8 99.8× 210.6×
CNN3 Int8 154.3× 209.9×

Int8 forward on CNN is only ~1.3–1.5× (already fast scalar loops; SIMD setup dominates).

Where SIMD does not help

  • Residual on 1³: forward chain has no skip tensor (skip=nil); residual is effectively identity. Timings are sub-microsecond — SIMD parallel add is pure overhead (amd64 Float64 fwd 4× slower with SIMD).
  • RNN/LSTM Int8 bwd: often ≈1× (scalar int8 backward already tight; BPTT serial).
  • Uint2 Dense fwd (amd64): occasional slower SIMD (~1.0×).

Reading a log line

Example (Dense Float32, amd64):

· Float32  PASS  fwd 78.7µs bwd 1.57ms simd fwd 49.4µs (37% faster (1.6×)) bwd 186.0µs (88% faster (8.5×)) loss 0.3312→0.3135  train 51.2ms
Field Meaning
Path / first PASS columns LayerUsesNativeExact and fwd/bwd/train gates
fwd / bwd Scalar native-exact micro-benchmark
simd fwd / simd bwd Same with SetSimdForward(true) → *_native_simd.go
loss₀→lossₙ First vs last of 30 training epochs
train Wall time for full 30-epoch train on that dtype

Table footer per layer: Dense native: 21 passed · 0 failed (of 21 dtypes).

Relationship to other docs

Topic Doc
QAT-like vs native exact training.md, quantization.md
Plan 9 SIMD kernels simd.md
Menu [7] SC/MC/SIMD parity + save/reload bedrock_validation.md
Parity symbols / log layout testing_and_validation.md

Reproduce

cd lucy_bloom_rivers && go run .
# [14] → pick layer [1]–[10] or [0] for full matrix

Requires GOARCH=amd64 or arm64 with AVX2/NEON linked (poly/simd) for SIMD columns. On other arches, native scalar paths still run; SIMD timing columns are omitted when Plan9SimdForwardForLayer is false.