release: 0.33.0 — GRU, upsample2d Bilinear export, autodiff coverage fix

CHANGELOG.md

@@ -2,6 +2,47 @@
 
 ## [Unreleased]
 
+## [0.33.0] - 2026-06-29
+
+### Added
+
+- **GRU layer (`sk.ainet.lang.nn.Gru`).** SKaiNET's first recurrent layer (issue #217): single-layer,
+  unidirectional, batch-first `[B, S, D] -> [B, S, H]`, PyTorch gate order (reset, update, new). Built
+  by composing existing primitives (matmul/add/sigmoid/tanh/narrow/concat) **unrolled over the static
+  sequence length at trace time** — StableHLO has no loop construct, so any recurrence must unroll. It
+  runs eagerly, is trainable through the standard tape, and exports to StableHLO with no dedicated
+  converter. Also adds a `gru(hiddenSize) { … }` network-DSL builder. (PR #772)
+- **`upsample2d` Bilinear + StableHLO export.** Adds the Bilinear forward (PyTorch coord map, 4-neighbour
+  blend) and its autodiff backward, and a traceable StableHLO lowering for **both** Nearest and Bilinear
+  (scale is static at trace time, so everything lowers to fixed reshape/broadcast/`dot_general` — no
+  runtime index math, no `custom_call`). Unblocks export of resize/FPN-style paths. (PR #771)
+- **Seven newly-differentiable ops.** `cos`, `sin`, `tril`, `gather`, `indexSelect`, `unfold`,
+  `convTranspose1d` now carry `@Diff` and have backward rules (with finite-difference parity tests):
+  trig for RoPE, `gather` for embedding lookup, `tril` for causal masks, the rest structural. (PR #774)
+- **KSP-generated autodiff-coverage guard.** The tracing-wrapper processor now emits
+  `DifferentiableTensorOpsRules.ruleNames` (the authoritative `@Diff` op set); a unit test asserts the
+  execution tape's dispatch covers it, so a differentiable op can no longer ship with a backward rule
+  that is never wired. `operators.json` now records `isDifferentiable` (+ optional `diffRuleName`),
+  schema-validated. (PR #774)
+
+### Fixed
+
+- **Silent gradient drop for `elu`, `leakyRelu`, `permute`.** These were `@Diff` and had correct
+  backward formulas, but had no arm in the execution tape's trace dispatch, so their gradients fell
+  through to `null` and were silently discarded. Now wired (and guarded by the coverage test above);
+  `permuteBackward` also fixed to decode its `axes` attribute as the traced `List<Int>`. (PR #774)
+- **`layerNorm` / `rmsNorm` / `batchNorm` lower to real `stablehlo.reduce`.** The norm converters
+  previously emitted non-compilable `reduce_mean` / `reduce_variance` `custom_call`s (export-only); they
+  now decompose to real `stablehlo.reduce`, so all three compile and run on stock IREE (llvm-cpu). (PR #769)
+
+### Changed
+
+- **BREAKING: `TensorOps.sin`, `TensorOps.cos`, `TensorOps.convTranspose1d` are now abstract.** They
+  previously had default `throw NotImplementedError(...)` bodies; they are abstract so the tracing
+  wrapper records them (and they become differentiable/exportable). Any type implementing `TensorOps`
+  directly must now override them — both bundled backends (`DefaultCpuOpsBase`, `VoidTensorOps`) already
+  do. (PR #774)
+
 ## [0.32.4] - 2026-06-26
 
 ### Fixed

README.md

@@ -36,17 +36,13 @@ Add the core dependencies (Gradle Kotlin DSL):
 ```kotlin
 dependencies {
     // Recommended: import the umbrella BOM and drop versions on the engine modules.
-    implementation(platform("sk.ainet:skainet-bom:0.32.4"))
+    implementation(platform("sk.ainet:skainet-bom:0.33.0"))
 
     implementation("sk.ainet.core:skainet-lang-core")
     implementation("sk.ainet.core:skainet-backend-cpu")
 }
 ```
 
-> The BOM was first correctly published to Maven Central in 0.22.2 — earlier versions
-> shipped at the wrong coordinates and could not be imported. Pin versions directly if
-> you need an older release.
-
 ### Hello Neural Net
 
 ```kotlin
@@ -241,6 +237,23 @@ Runnable examples:
 
 ---
 
+## What's New in 0.33.0
+
+- **GRU — the first recurrent layer.** `nn.Gru` (`[B,S,D]->[B,S,H]`, PyTorch gate order) composed from
+  existing primitives and unrolled over the static sequence at trace time, so it runs eagerly, trains
+  through the standard tape, and exports to StableHLO with no dedicated converter. Plus a `gru(…)`
+  network-DSL builder. (PR #772, issue #217)
+- **`upsample2d` Bilinear + StableHLO export** for both Nearest and Bilinear — everything lowers to fixed
+  reshape/broadcast/`dot_general` (no `custom_call`), unblocking resize/FPN-style export. (PR #771)
+- **Autodiff correctness + coverage.** Fixes a silent gradient-drop for `elu`/`leakyRelu`/`permute`
+  (backward rules existed but were never wired into the trace dispatch), makes `cos`/`sin`/`tril`/
+  `gather`/`indexSelect`/`unfold`/`convTranspose1d` differentiable, and adds a KSP-generated coverage
+  guard so a differentiable op can no longer ship without a wired backward. (PR #774)
+- **Norms compile on stock IREE.** `layerNorm`/`rmsNorm`/`batchNorm` now lower to real `stablehlo.reduce`
+  instead of export-only `custom_call`s. (PR #769)
+- **Breaking:** `TensorOps.sin`/`cos`/`convTranspose1d` are now abstract — backends implementing
+  `TensorOps` directly must override them (both bundled backends already do).
+
 ## What's New in 0.32.4
 
 - **Streaming detokenization keeps word spaces (`Tokenizer.decodeToken`).** Decoding generated tokens

gradle.properties

@@ -1,5 +1,5 @@
 GROUP=sk.ainet.core
-VERSION_NAME=0.32.4
+VERSION_NAME=0.33.0
 POM_DESCRIPTION=SKaiNET
 
 POM_URL=https://github.com/SKaiNET-developers/skainet/