//clika-runtime/io.clika.runtime/Tensors

Tensors

[jvm]
object Tensors

The tensor factories and data entry.

ClikaRtGen.load()
val x = Tensors.of(floatArrayOf(1f, 2f, 3f, 4f), longArrayOf(2, 2))
val z = Tensors.zeros(longArrayOf(2, 2), device = Device.CPU)
println((x + z).summary())

Dtype carriage is honest end to end: payload bytes enter at the declared dtype, and dtypes the JVM lacks ride BIT CARRIERS: ofFloat16Bits / ofBFloat16Bits take the 16-bit patterns (a Float.toBits()-style carrier, one Short per element); conversion runs in the runtime (ops.cast), never host-side float math.

Functions

Name	Summary
fromBlob	[jvm] @JvmOverloads fun fromBlob(buffer: ByteBuffer, shape: LongArray, dtype: Int = ClikaRtGen.DATA_TYPE_FLOAT32, device: Device? = null, deleter: VoidCallback? = null): Tensor Wrap memory the application owns, without copying: buffer must be a DIRECT `ByteBuffer` (`allocateDirect`, the one JVM buffer with a stable native address), holding shape elements of dtype in row-major order. With no deleter the tensor BORROWS the buffer, which must outlive every view of it; with a deleter the tensor ADOPTS it, and the deleter runs exactly once, when the last reference drops (on whichever thread that happens). Writes through the buffer are visible through the tensor: it is the same memory.
fromBlobStrided	[jvm] @JvmOverloads fun fromBlobStrided(buffer: ByteBuffer, shape: LongArray, strides: LongArray, dtype: Int = ClikaRtGen.DATA_TYPE_FLOAT32, device: Device? = null, deleter: VoidCallback? = null): Tensor fromBlob over a strided layout: strides are element strides per dimension (a region of a larger row-major image keeps the image's row pitch as its row stride). buffer's position 0 is the first element (`slice()` a buffer to start elsewhere).
fromBytes	[jvm] @JvmOverloads fun fromBytes(bytes: ByteArray, shape: LongArray, dtype: Int, device: Device? = null): Tensor Raw entry: bytes interpreted at dtype in row-major order.
full	[jvm] @JvmOverloads fun full(shape: LongArray, value: Double, dtype: Int = ClikaRtGen.DATA_TYPE_FLOAT32, device: Device? = null): Tensor A shape-extent tensor filled with value.
of	[jvm] @JvmOverloads fun of(data: DoubleArray, shape: LongArray, device: Device? = null): Tensor Float64 entry. [jvm] @JvmOverloads fun of(data: FloatArray, shape: LongArray, device: Device? = null): Tensor Float32 entry: data copied in row-major order over shape. [jvm] @JvmOverloads fun of(data: IntArray, shape: LongArray, device: Device? = null): Tensor Int32 entry. [jvm] @JvmOverloads fun of(data: LongArray, shape: LongArray, device: Device? = null): Tensor Int64 entry.
ofBFloat16Bits	[jvm] @JvmOverloads fun ofBFloat16Bits(bits: ShortArray, shape: LongArray, device: Device? = null): Tensor BFloat16 entry from the 16-bit patterns (bit-carrier form).
ofFloat16Bits	[jvm] @JvmOverloads fun ofFloat16Bits(bits: ShortArray, shape: LongArray, device: Device? = null): Tensor Float16 entry from the 16-bit patterns (the bit-carrier form, one IEEE-754 binary16 pattern per `Short`).
ones	[jvm] @JvmOverloads fun ones(shape: LongArray, dtype: Int = ClikaRtGen.DATA_TYPE_FLOAT32, device: Device? = null): Tensor A shape-extent tensor of ones.
zeros	[jvm] @JvmOverloads fun zeros(shape: LongArray, dtype: Int = ClikaRtGen.DATA_TYPE_FLOAT32, device: Device? = null): Tensor A shape-extent tensor of zeros.

Functions​

Functions