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feat(gemma): optional maxInferenceLen on load() to cap KV cache on constrained devices (#178) #180

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Merged
michalharakal merged 1 commit into from
Jun 15, 2026
+9 −5
Merged

feat(gemma): optional maxInferenceLen on load() to cap KV cache on constrained devices (#178) #180

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14 changes: 9 additions & 5 deletions llm-inference/gemma/src/commonMain/kotlin/sk/ainet/models/gemma/GemmaNetworkLoader.kt
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Original file line number Diff line number Diff line change
Expand Up @@ -120,7 +120,8 @@ public class GemmaNetworkLoader @PublishedApi internal constructor(
* Load weights and build a fully initialized DSL network.
*/
public suspend inline fun <reified T : DType, V> load(
ctx: ExecutionContext
ctx: ExecutionContext,
maxInferenceLen: Int? = null,
): Module<T, V> {
val rawWeights: Gemma4Weights<T, V> = when (val wp = weightsProvider) {
is WeightsProvider.GgufSource -> {
Expand Down Expand Up @@ -160,14 +161,15 @@ public class GemmaNetworkLoader @PublishedApi internal constructor(
rawWeights
}

return applyWeightsToNetwork(ctx, weights)
return applyWeightsToNetwork(ctx, weights, maxInferenceLen)
}

@PublishedApi
internal inline fun <reified T : DType, V> applyWeightsToNetwork(
ctx: ExecutionContext,
weights: Gemma4Weights<T, V>
): Module<T, V> = applyWeightsToNetworkNonReified(ctx, weights, T::class, debug)
weights: Gemma4Weights<T, V>,
maxInferenceLen: Int? = null,
): Module<T, V> = applyWeightsToNetworkNonReified(ctx, weights, T::class, debug, maxInferenceLen)
}

/** Shared non-reified impl used by both the inline-reified companion helpers
Expand All @@ -177,7 +179,8 @@ internal fun <T : DType, V> applyWeightsToNetworkNonReified(
ctx: ExecutionContext,
weights: Gemma4Weights<T, V>,
dtype: kotlin.reflect.KClass<T>,
debug: Boolean
debug: Boolean,
maxInferenceLen: Int? = null,
): Module<T, V> {
// Enable optional Gemma 4 features iff the checkpoint actually carries
// their weights. Real Gemma 4 GGUFs do; synthetic toy-model tests do not,
Expand All @@ -197,6 +200,7 @@ internal fun <T : DType, V> applyWeightsToNetworkNonReified(
val model = gemmaNetwork<T, V>(
weights.metadata,
dtype,
maxInferenceLen = maxInferenceLen ?: minOf(weights.metadata.contextLength, 4096),
qkNorm = hasQKNorm,
sandwichNorms = hasSandwichNorms,
layerOutputScale = hasLayerOutputScale,
Expand Down
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