ai.onnx.Range

ai.onnx · standard ONNX operator · ONNX opset ≥ 27

Description

Generates a 1-D tensor of numbers starting at start, incrementing by delta, up to but not including limit. The output length is max(ceil((limit - start) / delta), 0), and element i equals start + i * delta. Float16 inputs use float32 intermediate arithmetic when stash_type is 1, then cast each result back to float16.

See the ONNX Range spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
start start T 0 Scalar first value in the output sequence. required
limit limit T 0 Scalar exclusive upper bound of the sequence. required
delta delta T 0 Scalar step size between consecutive output values. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
output output T 1 1-D tensor containing the generated range of values, with the same dtype as the inputs. required

Attributes

Default values (overridable per request):

Attribute Default Description
stash_type 1 TensorProto element type used for float16 intermediate arithmetic; this implementation supports the standard float32 value (1). It has no effect for float32 and int32 inputs.

Type constraints

Variable Allowed dtypes
T float32, float16, int32

Files

Use with @huggingface/kernels

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit outputs entries provide shape and logical dtype metadata for the results listed below:

  • output

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model 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.Range", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
  start: { data: startData, shape: [] },
  limit: { data: limitData, shape: [] },
  delta: { data: deltaData, shape: [] },
}, {
  outputs: { output: { shape: [1], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.