ai.onnx.ReduceMin
ai.onnx · standard ONNX operator · ONNX opset ≥ 20
Description
Computes the minimum of input tensor elements along the specified axes. The output rank matches the input when keepdims is 1; otherwise reduced dimensions are pruned. Reduction over an empty set yields positive infinity when the dtype supports it, or the dtype's maximum value otherwise. For Boolean inputs, false is less than true.
See the ONNX ReduceMin spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
data |
x |
T |
— | — | The input tensor to reduce. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
reduced |
y |
T |
derived | — | The reduced output tensor containing minimum values. | required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
keepdims |
1 |
If 1, retains the reduced dimensions with size 1 in the output; if 0, those dimensions are pruned. |
noop_with_empty_axes |
0 |
When axes is empty: if 0 (default) reduces over all axes; if 1 the op acts as an identity (no-op reduction). |
axes |
[] |
Values of the optional ONNX axes tensor input, supplied through this request attribute; an empty list follows noop_with_empty_axes. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, uint32, int8, uint8, bool |
Device requirements
Some implementation variants require subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesdatamove-flat-copy-x4-tail.wgsl.jinjareduce-axis-split-reduce.wgsl.jinjareduce-axis0-splitk-combine.wgsl.jinjareduce-axis0-splitk-reduce.wgsl.jinjareduce-axis0-tilecols.wgsl.jinjareduce-flat-partial.wgsl.jinjareduce-narrow-empty-identity.wgsl.jinjareduce-noop-empty-axes.wgsl.jinjareduce-row-subgroup.wgsl.jinjareduce-row-tree.wgsl.jinjareduce-serial-axis.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:
y
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.ReduceMin", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [] } }, {
outputs: { y: { shape: [], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.