ai.onnx.Pow
ai.onnx · standard ONNX operator · ONNX opset ≥ 15
Description
Computes elementwise exponentiation Z = X ^ Y, applying f(x) = x^y to each element. Supports multidirectional (NumPy-style) broadcasting between X and Y.
See the ONNX Pow spec for the reference semantics.
Inputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
x |
X |
T |
— | — | Base tensor; first operand of the exponentiation. | required |
y |
Y |
U |
— | — | Exponent tensor; power applied to each base element. | required |
Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
z |
Z |
T |
derived | broadcast result of x and y |
Output tensor containing X raised to the power Y, same type as X. |
required |
Runtime arguments
| Name | Kind | Description | Presence |
|---|---|---|---|
constantY |
f32 |
Optional host-known constant exponent. When the exponent tensor y holds one integer value in [-16, 16] that the caller also passes here, a specialized integer-power kernel is selected. |
optional |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32 |
U |
float32, float16, int32, int16, uint32, int8, uint8 |
Files
metadata.json— kernel metadata (id, digests, per-variant templates, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casespow-scalar-exponent-vec4.wgsl.jinjapow-vec4.wgsl.jinjapow.wgsl.jinja
Use with @huggingface/kernels
npm install --save-exact @huggingface/kernels@0.0.1-preview.2
Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.
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.Pow", { version: 1 });
const { z } = await kernel({ x: { data: xData, shape: [1] }, y: { data: yData, shape: [1] } });
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Requires WebGPU support. See the compatibility table.