Hugging Face's logo

Kernels:
webgpu-kernels
/
ai.onnx.Unique

Kernel card Files
xet
 Community

ai.onnx.Unique

ai.onnx · standard ONNX operator · ONNX opset ≥ 11

Description

Finds unique values or subtensors along an optional axis. Without an axis, X is flattened; results are sorted or retain first-occurrence order. Sub-32-bit integers and booleans use lossless widened 32-bit storage. Metadata outputs remain logical int64 but use lossless uint32 storage because all values are bounded by an addressable tensor extent. Exact data-dependent output shapes must be supplied. ONNX-permitted uint16, 64-bit, string, and complex inputs are unsupported by this package.

See the ONNX Unique spec for the reference semantics.

Inputs

Name Upstream name Logical dtype Rank Shape Description Presence
x X T — — The N-D input tensor from which unique values or subtensors are extracted. When axis is omitted, tensors of any rank are flattened in row-major order. required

Outputs

Name Upstream name Logical dtype WebGPU storage Rank Shape Description Presence
y Y T runtime-selected; narrow integers and bool use 32-bit slots derived — Tensor containing all unique values or subtensors of X, sorted or in first-occurrence order. required
indices — I uint32 1 — Optional logical int64 indices of each Y value or slice's first occurrence in X; stored as bounded uint32 values by WebGPU. optional
inverse_indices — I uint32 1 — Optional logical int64 mapping from each flattened input value, or each input-axis slice, to its corresponding index in Y; stored as bounded uint32 values by WebGPU. optional
counts — I uint32 1 — Optional logical int64 occurrence count for each unique value or slice in Y; stored as bounded uint32 values by WebGPU. optional

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
axis — Optional axis along which unique subtensors are identified. Negative values count from the back; when omitted, the input is flattened.
sorted 1 Whether to sort unique elements in ascending order before output; 1 (default) sorts, 0 retains first-occurrence order.

Type constraints

Variable Allowed dtypes
T float32, float16, uint32, int32, int16, uint8, int8, bool
I int64

Implementation variants

One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers.

  • single_class_y — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_y_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_y_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_indices — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_indices_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_indices_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_inverse — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_inverse_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_inverse_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_indices_inverse — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_indices_inverse_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_indices_inverse_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_counts — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_counts_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_counts_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_indices_counts — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_indices_counts_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_indices_counts_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_inverse_counts — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_inverse_counts_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_inverse_counts_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • single_class_indices_inverse_counts — Copy the first representative and materialize requested metadata when the exact output contract proves there is one distinct class.
  • scalar_hash_parallel_indices_inverse_counts_unsorted — Hash first occurrences, scan flag blocks in parallel, and scatter scalar representatives in input order with only the requested metadata.
  • scalar_hash_parallel_indices_inverse_counts_sorted — Hash and compact scalar representatives in parallel, sort their order with shared and global bitonic stages, and materialize only the requested metadata.
  • flat_parallel_metadata_indices — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_inverse — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_indices_inverse — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_counts — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_indices_counts — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_inverse_counts — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_parallel_metadata_all — Builds the unique values with the parallel dedup and compaction passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_indices — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_inverse — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_indices_inverse — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_counts — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_indices_counts — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_inverse_counts — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.
  • flat_hash_metadata_all — Builds the unique values with the hash-set dedup passes, then resolves every metadata output from the finished result: one thread per input element for the bucket and the first-occurrence store, one thread per unique value for the tally.

Files

Use with @huggingface/kernels 

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

  • y

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version. It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of version.

Replace each *Data placeholder with a typed array containing the corresponding input data.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.Unique", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [1] } }, {
  outputs: { y: { shape: [1], dtype: "float32" } },
});
Downloads last month
-
kernel
webgpu
wgsl
apache-2.0
WebGPU

Requires WebGPU support. See the compatibility table.