ai.onnx.Squeeze
ai.onnx · standard ONNX operator · ONNX opset ≥ 13
Description
Removes single-dimensional entries from the shape of a tensor. If axes is provided, only those dimensions are removed; if omitted, all size-1 dimensions are removed. Selecting an axis whose size is not 1 raises an error.
See the ONNX Squeeze spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|---|
data |
data |
T |
runtime-selected; narrow integers and bool use 32-bit slots | — | — | Input tensor with at least as many dimensions as the largest axis index specified. | required |
axes |
axes |
S |
int32 |
1 |
— | Logical int64 1-D tensor of axis indices to squeeze; negative values count from the back, signed axes use int32 WebGPU storage, and omission squeezes all size-1 dimensions. | optional |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
squeezed |
squeezed |
T |
— | — | Reshaped tensor with the same data as the input but with the specified size-1 dimensions removed. | required |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, uint32, int16, uint8, int8, 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 casesdatamove-elementwise-copy.wgsl.jinjadatamove-flat-copy.wgsl.jinja
Use with @huggingface/kernels
The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.
The explicit outputs entries provide shape and logical dtype metadata for the results listed below:
squeezed
Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.
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.Squeeze", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { squeezed } = await kernel({ data: { data: dataData, shape: [1, 1, 1, 1] } }, {
outputs: { squeezed: { shape: [], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.