release: 0.53.0

CHANGELOG.md

@@ -2,8 +2,32 @@
 
 ## [Unreleased]
 
+## [0.53.0] - 2026-09-02
+
+Headline: **the export pipeline emits billion-parameter models.** Tracing a 4.5B-parameter
+Gemma 3n E2B through the DSL → tape → StableHLO path could not finish on a 48 GB host
+([#1247](https://github.com/SKaiNET-developers/SKaiNET/issues/1247)): shape-only tracing
+materialized real zero buffers, constant extraction copied every weight while the originals
+stayed live, and when the converter did fail it said so in MLIR comments and exited 0 — so the
+first broken node cascaded through a thousand more and still produced a "module". Each of those
+is closed, and the last one turned out to hide a fourth: the tied embedding is exactly one byte
+larger than a JVM array can hold, which no amount of widening could fix — external constants now
+travel as the aliased float array they already are. The same repro that OOMed a 46 GB heap now
+exports the full model in under a minute with zero failure comments, and the sharded SafeTensors
+loader the transformers families kept re-implementing lives in the engine.
+
 ### Added
 
+- **`ShardedSafeTensorsParametersLoader` — the sharded-index SafeTensors loader**
+  ([#1246](https://github.com/SKaiNET-developers/SKaiNET/issues/1246),
+  [#1252](https://github.com/SKaiNET-developers/SKaiNET/pull/1252)): a `ParametersLoader` over
+  `model.safetensors.index.json`, riding `StreamingShardedSafeTensorsReader.openFromIndex` with the
+  single-file loader's BF16/FP16 policies (`withPolicy` parity), a fail-fast dtype pre-scan before
+  any tensor is delivered, and a `tensorFilter` hook so size guards and name allowlists stay
+  family-side while every dtype decision stays in the engine. The dtype dispatch and dequant
+  helpers moved into a shared internal `SafeTensorsMaterializer`, so both loaders materialize
+  identically (parity-tested). Downstream: SKaiNET-transformers' hand-rolled per-family SafeTensors
+  loaders can collapse onto it.
 - **`BufferHandle.Floats` — array-free path for ≥2 GiB constants**
   ([#1247](https://github.com/SKaiNET-developers/SKaiNET/issues/1247)): external FP32 constants
   now ride the aliased `FloatArray` end-to-end (graph → `ExternalParameterRef` → `.irpa`), never
@@ -16,6 +40,14 @@
 
 ### Changed
 
+- **Void tracing allocates nothing**
+  ([#1247](https://github.com/SKaiNET-developers/SKaiNET/issues/1247),
+  [#1249](https://github.com/SKaiNET-developers/SKaiNET/pull/1249)): `VoidTensorOps` recorded every
+  shape-propagation op into a real dense zero buffer, allocated twice and retained by the trace —
+  ~9 GB of zeros across a 30-layer Gemma 3n E2B trace. Static-shape void results are now lazy
+  placeholders (readers still see zeros; unread ops allocate nothing), `ShapeOnlyTensorData` is
+  public for consumers that hand-roll shape-only data, and `matmulWeightTransposed` no longer
+  constructs the transposed intermediate. A 4096×4096 traced op stack tracks 0 bytes.
 - **Graph constants alias live weights; packed params fail loudly**
   ([#1247](https://github.com/SKaiNET-developers/SKaiNET/issues/1247)): `TraceToGraphBuilder` no
   longer copies every frozen float weight into the graph — the constant's `initial_value` aliases

README.md

@@ -51,7 +51,7 @@ 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:0.52.0"))
+    implementation(platform("sk.ainet:skainet-bom:0.53.0"))
 
     implementation("sk.ainet.core:skainet-lang-core")
     implementation("sk.ainet.core:skainet-backend-cpu")
@@ -308,29 +308,27 @@ val withoutLabel = dataPipeline<RawDataset>()
 
 ---
 
-## What's New in 0.52.0
-
-The engine stops silently running on the scalar floor:
-
-- **Self-healing kernel dispatch** — `KernelDispatch` installs itself on first use through the new
-  `ViewKernelPack` SPI, so an application that never called an install routine no longer loses
-  every kernel and falls back to the decoding reference path. When a fallback does happen, it now
-  says so once, loudly, instead of vanishing into a no-op trace sink.
-- **Ternary/BitNet kernels in the discovery set** — the `BITNET_B1_58` LUT gemv and the fused
-  `BITNET_PLANES` lm_head packs are ServiceLoader-discovered like the Q-series, so a BitNet
-  consumer gets the vendored NeoGPU kernels with zero bootstrap code. Validated downstream:
-  SKaiNET-transformers decodes BitNet-2B4T at full speed on discovery alone.
-- **Dense FP32 from any storage kind** — mapped and off-heap weights were dequantizing because the
-  kernel serving them only recognised `Heap`, which defeated the point of memory-mapped staging.
-- **Android native across the chain** — `androidNativeArm32`/`Arm64` now build and publish from the
-  whole downstream dependency graph, not just `skainet-io-core`, so on-device consumers can
-  actually resolve what they need.
-- **Faster decode-shaped matmul** — a dense FP32 GEMV path for the m ≤ 8 shapes decode issues
-  (16.7x at m=1), a direct-loop path for small work, and a cached weight transpose. ~2.3x decode
-  and prefill measured end to end on a downstream Gemma 4 port.
-- **Gemma 4 loads through the engine's own routes** — `gemma4` is registered in `TokenizerFactory`
-  and `ModelArchitecture`, and `SpecialTokenSplitter` no longer drops word boundaries when
-  decoding token by token.
+## What's New in 0.53.0
+
+The export pipeline emits billion-parameter models:
+
+- **Full Gemma 3n E2B export** — the DSL → tape → StableHLO path traced a 4.5B-parameter model
+  into an OOM at a 46 GB heap; the same repro now exports the whole model in under a minute.
+  Shape-only tracing no longer materializes zero buffers, graph constants alias the live weights
+  instead of copying them, and ≥2 GiB constants travel as `BufferHandle.Floats` — an aliased
+  float array — because the tied embedding is exactly one byte over what a JVM byte array holds.
+- **Conversion fails loudly** — `StableHloConverter` is strict by default: an unconvertible node
+  throws `HloConversionException`, an unresolved operand throws `MissingOperandException`, and a
+  packed weight reaching constant extraction throws `PackedConstantException` instead of silently
+  becoming a function argument. `ConversionErrorPolicy.LENIENT` restores the old
+  comment-and-continue behavior for inspection.
+- **Sharded SafeTensors in the engine** — `ShardedSafeTensorsParametersLoader` consumes
+  `model.safetensors.index.json` with the single-file loader's BF16/FP16 policies, a fail-fast
+  dtype pre-scan, and a `tensorFilter` hook; the per-family hand-rolled loaders downstream can
+  collapse onto it.
+- **Registry gaps the strictness surfaced** — `clamp` and the camelCase `indexSelect` the tracer
+  actually emits now lower, and `createBasic` registers the neural-net converter like
+  `createExtended` does.
 
 See [CHANGELOG.md](CHANGELOG.md) for full release notes, including every prior release.
 
@@ -356,6 +354,12 @@ We love contributions! Whether it's a new operator, documentation, or a bug fix:
 
 Browse the full codebase documentation on [DeepWiki](https://deepwiki.com/SKaiNET-developers/SKaiNET).
 
+### Contributors (0.53.0)
+
+- **Michal Harakal** ([@michalharakal](https://github.com/michalharakal)) — the billion-parameter
+  export arc: allocation-free void tracing, aliased constant extraction, strict StableHLO
+  conversion, the array-free `BufferHandle.Floats` path, and the sharded SafeTensors loader
+
 ### Contributors (0.52.0)
 
 - **Michal Harakal** ([@michalharakal](https://github.com/michalharakal)) — the Gemma 4 engine-gap

docs/antora.yml

@@ -15,7 +15,7 @@ asciidoc:
     framework_name: SKaiNET
     # Current SKaiNET release — bump once per release; referenced as
     # {skainet_version} in dependency snippets (blocks need subs="attributes+").
-    skainet_version: 0.52.0
+    skainet_version: 0.53.0
     ksp_version: 2.2.21-2.0.5
     dokka_version: 2.1.0
     asciidoctorj_version: 3.0.0

docs/modules/ROOT/pages/reference/kernel-support-matrix.adoc

@@ -1,7 +1,7 @@
 = Kernel × platform support matrix
 :description: Which compute-kernel provider serves each weight format on each KMP target.
 
-Generated from `kernel-support.json` (version `0.52.0`) by `KernelSupportMatrixTest` — registry introspection of the registered `KernelProvider` implementations. Do not edit by hand; run `./gradlew generateKernelMatrix` to refresh.
+Generated from `kernel-support.json` (version `0.53.0`) by `KernelSupportMatrixTest` — registry introspection of the registered `KernelProvider` implementations. Do not edit by hand; run `./gradlew generateKernelMatrix` to refresh.
 
 Each cell is the best (highest-priority) provider that serves `Float32 × format` `matmul` on that platform: *native-ffm* (100) → *panama-vector* (50) → *scalar* (0). An empty cell (`—`) means no provider carries a kernel there (the format is dequant-to-FP32 only).
 

docs/modules/ROOT/pages/reference/operators/generated/index.adoc

@@ -1,6 +1,6 @@
 = AI-NET Operators Reference
 
-Generated from version `0.52.0` on 2026-08-31
+Generated from version `0.53.0` on 2026-09-02
 
 == Operators by Modality
 

docs/modules/ROOT/pages/reference/ops-status-matrix.adoc

@@ -1,7 +1,7 @@
 = Operator Coverage Matrix
 :description: Cross-backend status for every operator function in SKaiNET.
 
-Generated from `operators.json` version `0.52.0` on 2026-08-31.
+Generated from `operators.json` version `0.53.0` on 2026-09-02.
 
 Rows are `Operator.function` pairs. The `Validated` column shows whether the function's documentation has been DARC-validated by a reviewer (see xref:contributing/darc-workflow.adoc[DARC workflow]). Remaining columns are backends that appear in any function's `statusByBackend` map — a missing entry means the backend makes no claim about the function (treat it as "unknown", not "not supported").
 

docs/modules/ROOT/pages/tutorials/android-classifier-getting-started.adoc

@@ -19,12 +19,12 @@ An Android project with Kotlin. Add the SKaiNET modules:
 [source,kotlin]
 ----
 dependencies {
-    implementation("sk.ainet.core:skainet-lang-core:0.52.0")     // tensors, DSL, training
-    implementation("sk.ainet.core:skainet-backend-cpu:0.52.0")   // CPU ops
-    implementation("sk.ainet.core:skainet-compile-dag:0.52.0")   // autograd (training context)
-    implementation("sk.ainet.core:skainet-data-api:0.52.0")      // Dataset / DataBatch
-    implementation("sk.ainet.core:skainet-data-simple:0.52.0")   // embedded Iris
-    runtimeOnly("sk.ainet.core:skainet-backend-jni-cpu:0.52.0")  // NEON kernels (see below)
+    implementation("sk.ainet.core:skainet-lang-core:0.53.0")     // tensors, DSL, training
+    implementation("sk.ainet.core:skainet-backend-cpu:0.53.0")   // CPU ops
+    implementation("sk.ainet.core:skainet-compile-dag:0.53.0")   // autograd (training context)
+    implementation("sk.ainet.core:skainet-data-api:0.53.0")      // Dataset / DataBatch
+    implementation("sk.ainet.core:skainet-data-simple:0.53.0")   // embedded Iris
+    runtimeOnly("sk.ainet.core:skainet-backend-jni-cpu:0.53.0")  // NEON kernels (see below)
 }
 ----
 

gradle.properties

@@ -1,5 +1,5 @@
 GROUP=sk.ainet.core
-VERSION_NAME=0.52.0
+VERSION_NAME=0.53.0
 POM_DESCRIPTION=SKaiNET
 
 POM_URL=https://github.com/SKaiNET-developers/skainet/