F.1 — finite-difference gradchecks for tanh, sigmoid, and the exact gated
composite (1-z)*m + z*c (z=sigmoid, c=tanh) added to tape_cpu_gradcheck.rs.
All pass (rel-err < 2e-2) — the activation VJPs that already existed are now
proven correct on the real CpuBackend (E0 discipline), so the gated cell can
rely on them.
F.2 — GatedMemoryUpdater: a GRU-style gated recurrent memory cell.
z = sigmoid([M‖x]·W_z); c = tanh([M‖x]·W_c); M_t = (1-z)⊙M + z⊙c
The convex update is a non-expansion (|M_t| ≤ max(|M_0|, 1)), so free rollout
stays bounded with NO clamp — and the learned gate can both jump at a regime
switch (z≈1) and hold+denoise in steady state (z≈0), which the residual+leaky
ClonedMemoryUpdater cannot. Same method surface (new/step/predict/train_step/
train_step_memory) so the omni-think facade is cell-generic.
Tests: gated cell trains (loss drops); 500-step free rollout stays in [-1,1]
without a clamp. clippy(-D)/fmt clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
End-to-end training on `Autodiff<CpuBackend>` (forward → backward →
extract grads → SGD → repeat), guarding the gradient-correctness fixes:
- `mlp_trains_and_loss_decreases`: 2-layer MLP (matmul + gelu), loss 8.65 → 0.39.
- `attention_set_encoder_learns_window_mean`: a minimal attention set-encoder
(the SMT predictive-state teacher shape) learns to predict its input window's
per-dim mean, loss 0.27 → ~0.0. The embedding fans out to Q/K/V and the
residual (4 uses), so this also regression-guards the fan-out gradient
accumulation fix inside a real attention block.
All ops used are gradient-checked in `tape_cpu_gradcheck.rs`.
Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
The decorator autograd (`Autodiff<B>`) had never been gradient-checked
against a real tensor backend — the entire test suite runs on a shape-only
`MockBackend` whose ops return their input, so they validate graph structure
but never gradient values. Running it through `CpuBackend` for the first time
(new `tests/tape_cpu_gradcheck.rs`, finite-difference checks) surfaced three
bugs that made the tape unusable for training; this fixes all three.
1. Double-free / UB in the dimension-erasure cast. The backward ops cast a
tensor to its runtime const-generic dimension via
`mem::transmute_copy::<_, TensorPrimitive<N>>(&src)` in ~100 sites. That
bit-copies the owned `Vec` without forgetting the source, so two values own
one buffer → double-free on any heap-backed backend (and Stacked-Borrows UB
from the typed pun). Replaced every site with a single `into_dim` helper
that is now **fully safe** — it round-trips through `to_data`/`from_data`
and rebuilds the shape with `array::from_fn`, no `unsafe` at all. (This is
why the whole repo previously bypassed the tape with analytic backward.)
2. Fan-out gradients were silently dropped. `accumulate_gradients` was a stub
that returned one path and discarded the other, and `AutodiffTensor::clone`
minted a fresh `TensorId`. Together, reusing a tensor (residuals,
`mul(s, s)`, shared Q/K/V — universal in transformers) split its gradient
across two ids and summed neither, yielding a fraction of the true value.
`accumulate_gradients` now sums via `B::add`; `clone` preserves the id so
fan-out paths collide on one sink.
3. Softmax backward panicked. `SoftmaxBackward` / `stable_softmax_backward`
subtracted a keep-dim row-sum from the full-shape grad, but the elementwise
backends assert equal shapes (no broadcasting). Added `broadcast_along_dim`
to tile the row-sum to full width first.
Verified: `tape_cpu_gradcheck` (matmul, fan-out add·mul, softmax) passes with
rel-err < 2e-2 vs central differences; full `rtx-autograd` suite green (263
passed, 0 failed); lib clippy `-D warnings` clean.
Known follow-up (out of scope): `cargo miri test` still aborts on a
Stacked-Borrows / integer-to-pointer violation inside `rtx-backend-cpu`'s
buffer internals — a grad-free `from_data`+`add`+`sum` probe reproduces the
identical error, so it is pre-existing backend UB, not an autograd issue.
Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
osobhandClaude Opus 4.8