| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.DynamicQuantizeLinear |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 11 |
|
|
| ## Description |
|
|
| Computes a per-tensor scale and zero point from the range of floating-point input `x`, extending the range to include zero, then quantizes each value to `uint8` as `saturate(round(x / y_scale) + y_zero_point)`. Uses round-to-nearest-even and clamps results to `[0, 255]`. |
|
|
| See the [ONNX `DynamicQuantizeLinear` spec](https://onnx.ai/onnx/operators/onnx__DynamicQuantizeLinear.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `x` | `T` | — | — | Float32 input tensor to quantize. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `y` | `TQ` | same as `x` | same as `x` | Quantized output tensor; same shape as the input. | required | |
| | `y_scale` | `T` | `0` | `[]` | Per-tensor scale factor derived from the input min/max range; scalar. | required | |
| | `y_zero_point` | `TQ` | `0` | `[]` | Per-tensor zero point for the quantization; scalar. | required | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32` | |
| | `TQ` | `uint8` | |
|
|
| ## Implementation variants |
|
|
| One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers. |
|
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| - `single_invocation` — Uses one invocation to find the range and quantize the tensor, avoiding partial buffers for small inputs. It also provides the fallback when the parallel reduction cannot satisfy device limits. |
| - `parallel_subgroup_reduce_vec4` — Reduces independent input blocks to min/max partials, combines them, and quantizes in a separate pass. The family uses packed reads when the input length is vec4-aligned. |
| - `parallel_subgroup_reduce` — Reduces independent input blocks to min/max partials, combines them, and quantizes in a separate pass. The family uses packed reads when the input length is vec4-aligned. |
| - `grid_stride_reduce_vec4` — Caps the number of min/max partials and grid-strides each workgroup across the input. This bounds scratch size and finalization work for large tensors. |
| - `grid_stride_reduce` — Caps the number of min/max partials and grid-strides each workgroup across the input. This bounds scratch size and finalization work for large tensors. |
|
|
| ## Files |
|
|
| - [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, provenance) |
| - [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth) |
| - [`test.json`](build/webgpu/test.json) — correctness cases |
| - [`bench.json`](build/webgpu/bench.json) — benchmark cases |
| - [`dynamic-quantize-linear-quantize.wgsl.jinja`](build/webgpu/dynamic-quantize-linear-quantize.wgsl.jinja) |
| - [`dynamic-quantize-linear-reduce.wgsl.jinja`](build/webgpu/dynamic-quantize-linear-reduce.wgsl.jinja) |
| - [`dynamic-quantize-linear.wgsl.jinja`](build/webgpu/dynamic-quantize-linear.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| 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. |
|
|
| ```js |
| import { getKernel } from "@huggingface/kernels"; |
| |
| const kernel = await getKernel("webgpu-kernels/ai.onnx.DynamicQuantizeLinear", { version: 1 }); |
| const { y, y_scale, y_zero_point } = await kernel({ x: { data: xData, shape: [1] } }); |
| ``` |
|
|
