library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.BitCast
ai.onnx · standard ONNX operator · ONNX opset ≥ 26
Description
Reinterprets the raw bit pattern of a tensor as a different data type without any value conversion. The target type must have the same bit-width as the input type, and the output tensor has the same shape as the input.
See the ONNX BitCast spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
input |
input |
T |
— | — | Input tensor to be bitwise reinterpreted. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
output |
U |
same as input |
same as input |
Output tensor with the same shape as the input, reinterpreted as the target type. | required |
Attributes
Attributes and default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
to |
— | Required TensorProto DataType enum integer naming the output dtype; the target type must have the same bit-width as the input type. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, int8, int32, uint8, uint32 |
U |
float32, int8, int32, uint8, uint32 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesbitcast.wgsl.jinja
Use with @huggingface/kernels
The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.
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.BitCast", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [] } }, {
attrs: { to: 6 },
});