ai.onnx.Size
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Returns the total number of elements in a tensor as a logical int64 scalar equal to the product of the input dimensions. The input may have any shape; supported element types are listed below.
See the ONNX Size spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
data |
data |
T |
— | — | Input tensor of arbitrary shape. | required |
Outputs
| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|---|
size |
size |
S |
uint32 |
0 |
[] |
Logical int64 scalar holding the total number of elements in the input tensor; WebGPU emits the bounded value as uint32. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, uint32, int16, int8, uint8, bool |
S |
int64 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casessize.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.Size", { version: 1 });
const { size } = await kernel({ data: { data: dataData, shape: [] } });
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kernel
webgpu
wgsl
apache-2.0
WebGPU
Requires WebGPU support. See the compatibility table.