Lodestar.Gpu 0.1.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

Uploads a row-major block of vectors to the accelerator.

public static DeviceEmbeddingMatrix Upload(GpuContext context, ReadOnlySpan<float> rows, int count, int dimension, bool normalize = true)

Parameters — context is the accelerator to upload to. rows holds count × dimension values, row-major and contiguous. normalize L2-normalizes each row on upload, which is the default and what makes cosine a dot product.

Returns — DeviceEmbeddingMatrix, owning device memory the caller disposes.

Exceptions — ArgumentNullException when context is null; ArgumentOutOfRangeException when count or dimension is below 1; ArgumentException when rows is not exactly the block.

Example — the shape a caller writes.

using Lodestar.Gpu.Compute;

using var context = GpuContext.Create(preferCpu: true);
using var matrix = DeviceEmbeddingMatrix.Upload(
    context, [3f, 4f, 0f, 1f], count: 2, dimension: 2);

int held = matrix.Count;  // => 2

Remarks — the staging copy is deliberate: normalization happens on the host, on a copy, so the caller's array is never modified. That costs one pass and one allocation per upload, which is paid once per corpus rather than once per query.

Normalizing twice is the mistake to avoid. If the vectors are already unit length, pass normalize: false: re-normalizing is harmless arithmetically but costs a pass, and a caller who mixes normalized and unnormalized corpora in one index gets scores that are not comparable.

Applies to — net10.0, netstandard2.1.

See also — DeviceEmbeddingMatrix.