Merge branch 'master' into sign-op

inference/inference-core/src/commonMain/kotlin/io/kinference.core/operators/KIOperatorFactory.kt

@@ -128,6 +128,7 @@ object KIOperatorFactory : OperatorFactory<KIONNXData<*>> {
         "Shape" -> Shape(name, version, attributes, inputs, outputs)
         "Sigmoid" -> Sigmoid(name, version, attributes, inputs, outputs)
         "Sign" -> Sign(name, version, attributes, inputs, outputs)
+        "Sin" -> Sin(name, version, attributes, inputs, outputs)
         "Size" -> Size(name, version, attributes, inputs, outputs)
         "SkipLayerNormalization" -> SkipLayerNormalization(name, version, attributes, inputs, outputs)
         "Slice" -> Slice(name, version, attributes, inputs, outputs)

inference/inference-core/src/commonMain/kotlin/io/kinference.core/operators/activations/Sin.kt

@@ -0,0 +1,53 @@
+package io.kinference.core.operators.activations
+
+import io.kinference.attribute.Attribute
+import io.kinference.core.KIONNXData
+import io.kinference.graph.Contexts
+import io.kinference.ndarray.arrays.*
+import io.kinference.ndarray.extensions.activations.sin
+import io.kinference.operator.*
+import io.kinference.primitives.types.DataType
+
+sealed class Sin(
+    name: String,
+    info: OperatorInfo,
+    attributes: Map<String, Attribute<Any>>,
+    inputs: List<String>,
+    outputs: List<String>
+) : Activation(name, info, attributes, inputs, outputs) {
+    companion object {
+        private val DEFAULT_VERSION = VersionInfo(sinceVersion = 7)
+
+        operator fun invoke(name: String, version: Int?, attributes: Map<String, Attribute<Any>>, inputs: List<String>, outputs: List<String>) =
+            when (version ?: DEFAULT_VERSION.sinceVersion) {
+                in SinVer7.VERSION.asRange() -> SinVer7(name, attributes, inputs, outputs)
+                else -> error("Unsupported version of Sin operator: $version")
+            }
+    }
+}
+
+
+class SinVer7(
+    name: String,
+    attributes: Map<String, Attribute<Any>>,
+    inputs: List<String>,
+    outputs: List<String>
+) : Sin(name, INFO, attributes, inputs, outputs) {
+    companion object {
+        private val TYPE_CONSTRAINTS = FLOAT_DATA_TYPES
+
+        private val INPUT_INFO = listOf(IOInfo(0, TYPE_CONSTRAINTS, "input", optional = false))
+        private val OUTPUT_INFO = listOf(IOInfo(0, TYPE_CONSTRAINTS, "output", optional = false))
+
+        internal val VERSION = VersionInfo(sinceVersion = 7)
+        private val INFO = OperatorInfo("Sin", emptySet(), INPUT_INFO, OUTPUT_INFO, VERSION, OperatorInfo.DEFAULT_DOMAIN)
+    }
+
+    override suspend fun activate(input: NDArrayCore, contexts: Contexts<KIONNXData<*>>): NDArrayCore {
+        return when (val type = input.type) {
+            DataType.FLOAT -> (input as FloatNDArray).sin()
+            DataType.DOUBLE -> (input as DoubleNDArray).sin()
+            else -> error("Unsupported data type for this operation: $type")
+        }
+    }
+}

inference/inference-core/src/commonTest/kotlin/io/kinference/operators/activations/SinTest.kt

@@ -0,0 +1,19 @@
+package io.kinference.operators.activations
+
+import io.kinference.KITestEngine
+import io.kinference.utils.TestRunner
+import kotlin.test.Test
+
+class SinTest {
+    private fun getTargetPath(dirName: String) = "sin/$dirName/"
+
+    @Test
+    fun test_sin() = TestRunner.runTest {
+        KITestEngine.KIAccuracyRunner.runFromResources(getTargetPath("test_sin"))
+    }
+
+    @Test
+    fun test_sin_example() = TestRunner.runTest {
+        KITestEngine.KIAccuracyRunner.runFromResources(getTargetPath("test_sin_example"))
+    }
+}

inference/inference-tfjs/src/jsMain/kotlin/io.kinference.tfjs/operators/TFJSOperatorFactory.kt

@@ -111,6 +111,7 @@ object TFJSOperatorFactory : OperatorFactory<TFJSData<*>> {
         "Shape" -> Shape(name, version, attributes, inputs, outputs)
         "Sigmoid" -> Sigmoid(name, version, attributes, inputs, outputs)
         "Sign" -> Sign(name, version, attributes, inputs, outputs)
+        "Sin" -> Sin(name, version, attributes, inputs, outputs)
         "Size" -> Size(name, version, attributes, inputs, outputs)
         "SkipLayerNormalization" -> SkipLayerNormalization(name, version, attributes, inputs, outputs)
         "Slice" -> Slice(name, version, attributes, inputs, outputs)

inference/inference-tfjs/src/jsMain/kotlin/io.kinference.tfjs/operators/activations/Sin.kt

@@ -0,0 +1,53 @@
+package io.kinference.tfjs.operators.activations
+
+import io.kinference.attribute.Attribute
+import io.kinference.data.ONNXData
+import io.kinference.graph.Contexts
+import io.kinference.ndarray.arrays.NumberNDArrayTFJS
+import io.kinference.ndarray.extensions.sin
+import io.kinference.operator.*
+import io.kinference.tfjs.data.tensors.TFJSTensor
+import io.kinference.tfjs.data.tensors.asTensor
+
+sealed class Sin(
+    name: String,
+    info: OperatorInfo,
+    attributes: Map<String, Attribute<Any>>,
+    inputs: List<String>,
+    outputs: List<String>
+) : Operator<TFJSTensor, TFJSTensor>(name, info, attributes, inputs, outputs) {
+    companion object {
+        private val DEFAULT_VERSION = VersionInfo(sinceVersion = 7)
+
+        operator fun invoke(name: String, version: Int?, attributes: Map<String, Attribute<Any>>, inputs: List<String>, outputs: List<String>): Sin {
+            return when (version ?: DEFAULT_VERSION.sinceVersion) {
+                in SinVer7.VERSION.asRange() -> SinVer7(name, attributes, inputs, outputs)
+                else -> error("Unsupported version of Sin operator: $version")
+            }
+        }
+    }
+}
+
+class SinVer7(
+    name: String,
+    attributes: Map<String, Attribute<Any>>,
+    inputs: List<String>,
+    outputs: List<String>
+) : Sin(name, INFO, attributes, inputs, outputs) {
+    companion object {
+        private val TYPE_CONSTRAINTS = FLOAT_DATA_TYPES
+
+        private val ATTRIBUTES_INFO = emptyList<AttributeInfo>()
+
+        private val INPUT_INFO = listOf(IOInfo(0, TYPE_CONSTRAINTS, "input", optional = false))
+        private val OUTPUT_INFO = listOf(IOInfo(0, TYPE_CONSTRAINTS, "output", optional = false))
+
+        internal val VERSION = VersionInfo(sinceVersion = 7)
+        private val INFO = OperatorInfo("Sin", ATTRIBUTES_INFO, INPUT_INFO, OUTPUT_INFO, VERSION, OperatorInfo.DEFAULT_DOMAIN)
+    }
+
+    override suspend fun <D : ONNXData<*, *>> apply(contexts: Contexts<D>, inputs: List<TFJSTensor?>): List<TFJSTensor?> {
+        val input = inputs[0]!!.data as NumberNDArrayTFJS
+        return listOf(input.sin().asTensor("output"))
+    }
+}

inference/inference-tfjs/src/jsTest/kotlin/io/kinference/tfjs/operators/activations/SinTest.kt

@@ -0,0 +1,19 @@
+package io.kinference.tfjs.operators.activations
+
+import io.kinference.tfjs.runners.TFJSTestEngine.TFJSAccuracyRunner
+import io.kinference.utils.TestRunner
+import kotlin.test.Test
+
+class SinTest {
+    private fun getTargetPath(dirName: String) = "sin/$dirName/"
+
+    @Test
+    fun test_sin() = TestRunner.runTest {
+        TFJSAccuracyRunner.runFromResources(getTargetPath("test_sin"))
+    }
+
+    @Test
+    fun test_sin_example() = TestRunner.runTest {
+        TFJSAccuracyRunner.runFromResources(getTargetPath("test_sin_example"))
+    }
+}

ndarray/ndarray-core/src/commonMain/kotlin/io/kinference/ndarray/extensions/activations/PrimitiveSimpleActivations.kt

@@ -19,6 +19,7 @@ fun PrimitiveNDArray.cosh(): PrimitiveNDArray = applyElementWise { cosh(it) }
 
 fun PrimitiveNDArray.asin(): PrimitiveNDArray = applyElementWise { asin(it) }
 fun PrimitiveNDArray.asinh(): PrimitiveNDArray = applyElementWise { asinh(it) }
+fun PrimitiveNDArray.sin(): PrimitiveNDArray = applyElementWise { sin(it) }
 
 fun PrimitiveNDArray.atan(): PrimitiveNDArray = applyElementWise { atan(it) }
 fun PrimitiveNDArray.atanh(): PrimitiveNDArray = applyElementWise { atanh(it) }

ndarray/ndarray-core/src/commonMain/kotlin/io/kinference/ndarray/stubs/PrimitiveMathStubs.kt

@@ -11,6 +11,7 @@ internal fun cosh(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationE
 
 internal fun asin(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationException()
 internal fun asinh(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationException()
+internal fun sin(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationException()
 
 internal fun atan(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationException()
 internal fun atanh(x: PrimitiveType): PrimitiveType = throw UnsupportedOperationException()

ndarray/ndarray-tfjs/src/jsMain/kotlin/io.kinference.ndarray/core/Functions.kt

@@ -170,3 +170,5 @@ internal external val isInf: (x: ArrayTFJS) -> ArrayTFJS
 internal external val isNaN: (x: ArrayTFJS) -> ArrayTFJS
 
 internal external val sign: (x: ArrayTFJS) -> ArrayTFJS
+
+internal external val sin: (x: ArrayTFJS) -> ArrayTFJS

ndarray/ndarray-tfjs/src/jsMain/kotlin/io.kinference.ndarray/extensions/NDArrayExtension.kt

@@ -128,6 +128,8 @@ fun NumberNDArrayTFJS.atanh() = NumberNDArrayTFJS(tfjsArray.atanh())
 
 fun NumberNDArrayTFJS.tan() = NumberNDArrayTFJS(tfjsArray.tan())
 
+fun NumberNDArrayTFJS.sin() = NumberNDArrayTFJS(tfjsArray.sin())
+
 fun NumberNDArrayTFJS.moments(axis: Int, keepDims: Boolean = false) = tfjsArray.moments(axis, keepDims).toNDArray()
 
 fun NumberNDArrayTFJS.moments(axes: Array<Int>, keepDims: Boolean = false) = tfjsArray.moments(axes, keepDims).toNDArray()

ndarray/ndarray-tfjs/src/jsMain/kotlin/io.kinference.ndarray/extensions/TensorExtenstion.kt

@@ -300,3 +300,5 @@ internal fun ArrayTFJS.isNaN() = isNaN(this)
 internal fun ArrayTFJS.bandPart(numLower: Int = 0, numUpper: Int = 0) = linalg.bandPart(this, numLower, numUpper)
 
 internal fun ArrayTFJS.sign() = sign(this)
+
+internal fun ArrayTFJS.sin() = sin(this)

utils/utils-testing/src/commonMain/resources/sin/test_sin/descriptor.txt

@@ -0,0 +1,2 @@
+test_data_set_0/input_0.pb
+test_data_set_0/output_0.pb

utils/utils-testing/src/commonMain/resources/sin/test_sin/test_data_set_0/input_0.pb

@@ -0,0 +1 @@
+
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utils/utils-testing/src/commonMain/resources/sin/test_sin/test_data_set_0/output_0.pb

@@ -0,0 +1 @@
+
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utils/utils-testing/src/commonMain/resources/sin/test_sin_example/descriptor.txt

@@ -0,0 +1,2 @@
+test_data_set_0/input_0.pb
+test_data_set_0/output_0.pb