library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
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
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesrange.wgsl.jinja
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" } },
});