ai.onnx.Dropout
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Inference-only Dropout. Copies a floating-point tensor unchanged and optionally emits an all-true boolean mask. The ratio and training_mode inputs, seeded randomness, and training behavior are not supported by this package.
See the ONNX Dropout spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
data |
x |
T |
— | — | The input floating-point tensor to copy unchanged. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
y |
T |
same as data |
same as data |
The output tensor, same shape as the input; equals the input in inference mode. | required |
mask |
mask |
M |
same as data |
same as data |
Boolean mask indicating which elements were kept (all-ones in inference mode). | optional |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16 |
M |
bool |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesdropout-vec4.wgsl.jinjadropout.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.Dropout", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [] } });
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Requires WebGPU support. See the compatibility table.