library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
ai.onnx.BitShift
ai.onnx · standard ONNX operator · ONNX opset ≥ 11
Description
Performs an elementwise bitwise shift on unsigned integer tensors. X is shifted left or right by the amounts in Y, with the direction controlled by the direction attribute. Supports multidirectional (NumPy-style) broadcasting between X and Y.
See the ONNX BitShift spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
X |
x |
T |
— | — | Input tensor to be shifted. | required |
Y |
y |
T |
— | — | Tensor specifying the number of bit positions to shift each element of X. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
Z |
z |
T |
derived | broadcast result of X and Y |
Output tensor with the same shape as the broadcast result of X and Y. | required |
Attributes
Attributes and default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
direction |
— | Direction of the bit shift: LEFT shifts bits toward higher significance (increasing value), while RIGHT shifts toward lower significance (decreasing value). |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
uint32, uint8 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesbitshift-vec4.wgsl.jinjabitshift.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.BitShift", { version: 1 });
const { z } = await kernel({ x: { data: xData, shape: [2] }, y: { data: yData, shape: [2] } }, {
attrs: { direction: "RIGHT" },
});