Release/0.55.0

CHANGELOG.md

@@ -9,6 +9,38 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.55.0] — 2026-09-11
+
+A transformers-only release, same pattern as 0.54.1: no new engine version, still against
+**SKaiNET engine 0.54.0**. Adds a new `asr-domain` module and extends `BackendProvider`,
+generalizing plumbing that used to live downstream in one ASR cartridge family's repo.
+
+### Added — `asr-domain` module + `BackendProvider` capabilities/options (#432)
+
+- **New `asr-domain` module** (`sk.ainet.asr.domain`, publishes as
+  `skainet-transformers-asr-domain`): generic ASR task types — `Transcription`,
+  `TranscriptionTimings`, `StopReason`, `AsrEvent`, `DecodingOptions`, `FeatureFrames`. Moved up
+  from the downstream ASR cartridge ecosystem, where they lived only because that's where the
+  original `whisper-cli` extraction happened to put them, not because they're Whisper-specific —
+  both the Whisper and Moonshine cartridge families depend on these, and keeping them downstream
+  in one family's repo made the other structurally dependent on it for generic plumbing.
+  Framework-free (empty `commonMain` deps), full KMP target spread matching `llm-api`'s
+  convention for consumer-facing SPI modules (ios/linux/macos/jvm/js/wasm/android).
+- **`BackendProvider` gains `capabilities: BackendCapabilities`** (supported dtypes, compile
+  support, NPU usage, max sequence length — defaulted, so existing implementers don't need to
+  change) **and `createContext(options: BackendOptions = BackendOptions())`** (was
+  parameterless). `kllama`'s `CpuBackendProvider` updated to match.
+- **No second backend-registry module.** Downstream's own `backend-spi`
+  `ExecutionContextFactory`/`BackendRegistry` seam is *not* duplicated here — unified into this
+  existing `BackendProvider`/`BackendRegistry` instead, since both did the same job (select a
+  strategy producing a SKaiNET `ExecutionContext`). `BackendRegistry` itself is unchanged.
+- Verified: `:asr-domain:build`, `:llm-core:build`, `:llm-runtime:kllama:build` and the
+  corresponding `allTests` all green; `apiDump` regenerated for both `llm-core` and `kllama`'s
+  binary-compatibility-validator baselines. Downstream (`asr-whisper-iree-cartridge`, `asr-cli`)
+  verified against this branch via the `useLocalSkainet`-style opt-in composite substitution
+  pattern before this release — full `check` green in both, including a Docker smoke test of the
+  shipped `asr-cli` image.
+
 ## [0.54.1] — 2026-09-07
 
 A transformers-only release, same pattern as 0.40.2: no new engine version, still against

README.md

@@ -109,8 +109,22 @@ Honest status — see the project-status note at the top of this README.
 
 ## Current release
 
-The current release is **0.54.1** (against **SKaiNET 0.54.0** — a transformers-only release, same
-pattern as 0.40.2: no new engine version needed).
+The current release is **0.55.0** (against **SKaiNET 0.54.0** — a transformers-only release, same
+pattern as 0.54.1: no new engine version needed).
+
+**A new `asr-domain` module, and `BackendProvider` learns capabilities/options.** Generic ASR
+task types (`Transcription`, `DecodingOptions`, `FeatureFrames`, ...) move up from the downstream
+ASR cartridge ecosystem, where they lived only because that's where the original `whisper-cli`
+extraction happened to put them — both the Whisper and Moonshine cartridge families depend on
+these, so keeping them in one family's repo made the other structurally dependent on it for
+generic plumbing. `BackendProvider` gains a `capabilities: BackendCapabilities` property and a
+defaulted `createContext(options: BackendOptions = BackendOptions())` (was parameterless) —
+source-compatible with every existing implementer. Downstream's own execution-backend registry
+seam is *not* duplicated as a second module here; it's unified into this existing
+`BackendProvider`/`BackendRegistry` instead, since both already did the same job. (#432)
+
+It builds on **0.54.1**, which made attention heads run in parallel and stopped positional K/V
+caches from copying the whole prefix per layer per token.
 
 **Attention heads run in parallel.** `MultiHeadAttention` is the first consumer of the engine's
 SKEEP-005 `Schedule`: heads (or GQA groups) map onto cores via `AttentionSchedulePolicy`
@@ -218,7 +232,7 @@ The recommended way to consume is via the BOM. It pins every published `skainet-
 
 ```kotlin
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
 
     // Versions resolved from the BOM:
     implementation("sk.ainet.transformers:skainet-transformers-core")
@@ -250,6 +264,7 @@ dependencies {
 | `llm-agent`          | Chat templates, tool-call parsers, agent loops; Java surface.           |
 | `llm-apps`           | CLIs: `skainet-cli` (unified), `kllama-cli`, `kbert-cli`, plus `kllama-java-sample`. |
 | `llm-test/llm-test-java` | JUnit 5 end-to-end tests for the Java surface (gated on `TINYLLAMA_MODEL_PATH`). |
+| `asr-domain`         | Generic ASR task types (`Transcription`, `DecodingOptions`, `FeatureFrames`) — framework-free, no dependency on the rest of this repo. Consumed by downstream ASR cartridges (Whisper, Moonshine). |
 
 ## Supported targets
 
@@ -273,6 +288,7 @@ Which Maven artifact publishes which Kotlin target (derived from each module's
 | `llm-runtime/kapertus` | ✓ | — | — | — | — | — | — | — |
 | `llm-performance` | ✓ | ✓ | — | ✓ | — | ✓ | ✓ | ✓ |
 | `llm-providers`, `llm-apps/*`, `llm-test/*` | ✓ | — | — | — | — | — | — | — |
+| `asr-domain` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
 
 ¹ `transformer-core` additionally publishes `androidNativeArm32`/`androidNativeArm64`.
 ² `moonshine` publishes `iosArm64` and `androidNativeArm64` but no simulator or Android (AGP) variant.

docs/modules/ROOT/pages/reference/moonshine-encoder.adoc

@@ -24,7 +24,7 @@ a DSL decoder is future work.
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
     implementation("sk.ainet.transformers:skainet-transformers-inference-moonshine")
 }
 ----

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

@@ -55,7 +55,7 @@ dependencies {
     // self-registers it on ART at process start — nothing to call.
     runtimeOnly("sk.ainet.core:skainet-backend-jni-cpu")
 
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
     implementation("sk.ainet.transformers:skainet-transformers-core")
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-inference-llama")

docs/modules/ROOT/pages/tutorials/getting-started-java.adoc

@@ -25,7 +25,7 @@ In your `build.gradle.kts`:
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
 
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-agent")
@@ -41,7 +41,7 @@ Or in Maven (Maven needs the `-jvm` classifier suffix on platform artifacts):
     <dependency>
       <groupId>sk.ainet.transformers</groupId>
       <artifactId>skainet-transformers-bom</artifactId>
-      <version>0.54.1</version>
+      <version>0.55.0</version>
       <type>pom</type>
       <scope>import</scope>
     </dependency>

docs/modules/ROOT/pages/tutorials/getting-started-leaf.adoc

@@ -34,7 +34,7 @@ encoder output to the advertised dimensionality. The runtime applies it automati
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
     implementation("sk.ainet.transformers:skainet-transformers-providers")
 }
 ----

docs/modules/ROOT/pages/tutorials/llama3-tool-calling.adoc

@@ -52,7 +52,7 @@ The pieces you need live in three modules:
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.54.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.55.0"))
 
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-agent")

docs/modules/ROOT/pages/tutorials/parallel-attention-getting-started.adoc

@@ -10,7 +10,7 @@ schedule only decides where the work runs.
 
 * JDK 21+ (the JVM is the only target with a parallel schedule; others run `Schedule.Sequential`).
 * A Llama 3.2 or Qwen3 GGUF, for example `Llama-3.2-1B-Instruct-Q8_0.gguf`.
-* SKaiNET-transformers 0.54.1 or later.
+* SKaiNET-transformers 0.55.0 or later.
 
 == Step 1: Dependencies
 

gradle.properties

@@ -1,5 +1,5 @@
 GROUP=sk.ainet.transformers
-VERSION_NAME=0.54.1
+VERSION_NAME=0.55.0
 
 POM_DESCRIPTION=SKaiNET-transformers
 

llm-inference/gemma/README.md

@@ -4,7 +4,7 @@ Reusable **Gemma** model (incl. the FunctionGemma tool-calling fine-tune) author
 a portable graph producer with **no runtime/board/Torq code**. Pair it with the runtime module below to
 decode on-device.
 
-- **Coordinate:** `sk.ainet.transformers:skainet-transformers-inference-gemma:0.54.1`
+- **Coordinate:** `sk.ainet.transformers:skainet-transformers-inference-gemma:0.55.0`
 - **Targets:** `android`, `iosArm64`, `iosSimulatorArm64`, `macosArm64`, `linuxX64`, `linuxArm64` (broadly
   portable — mobile through server).
 - **Entry point:** `gemmaNetwork()` / `GemmaNetworkLoader` (loads a GGUF, builds the DSL graph, incl. the
@@ -24,8 +24,8 @@ FunctionGemma has a one-liner facade in `…:skainet-transformers-runtime-kgemma
 
 ```kotlin
 dependencies {
-    implementation("sk.ainet.transformers:skainet-transformers-inference-gemma:0.54.1")
-    implementation("sk.ainet.transformers:skainet-transformers-runtime-gemma-iree:0.54.1") // on-device decode
+    implementation("sk.ainet.transformers:skainet-transformers-inference-gemma:0.55.0")
+    implementation("sk.ainet.transformers:skainet-transformers-runtime-gemma-iree:0.55.0") // on-device decode
 }
 ```