---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# com.microsoft.CausalConvWithState

`com.microsoft`  ·  ONNX Runtime contrib operator  ·  contrib since_version 1

## Description

Legacy Microsoft contrib form of stateful 1-D causal depthwise convolution. Each channel uses its own `(channels, 1, kernel)` weight over current and past positions, with optional activation and `past_state`/`present_state` tensors for incremental decoding. The contrib-only `state_window` attribute may retain several rollback states. This inference implementation preserves the existing contrib ABI with `ndim = 1`, float16 or float32 tensors, and float32 accumulation; spatial ranks 2 and 3 and bfloat16 are not implemented.

See the [ONNX Runtime `CausalConvWithState` contrib-operator spec](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.CausalConvWithState) for the reference semantics.

## Inputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `input` | `inputT` | `T` | `3` | — | Channels-first input tensor with shape `(batch_size, channels, sequence_length)` for the supported 1-D mode. | required |
| `weight` | `weightT` | `T` | `3` | — | Depthwise convolution kernel with shape `(channels, 1, kernel_size)` for the supported 1-D mode. | required |
| `bias` | `biasT` | `T` | `1` | — | Optional per-channel bias with shape `(channels,)`. | optional |
| `past_state` | `pastStateT` | `T` | derived | — | Carry state from the previous step; shape `(batch_size, channels, k_1 - 1)`, or `(W, batch_size, channels, k_1 - 1)` when `state_window = W > 0`, in which case only slot `W - 1` is read. If absent, the left-side padding is zero. | optional |

## Outputs

| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `outputT` | `T` | `3` | same as `input` | Convolution output with the same shape as `input`. | required |
| `present_state` | `presentStateT` | `T` | derived | derived; see description | Updated carry state; shape `(batch_size, channels, k_1 - 1)`, or `(W, batch_size, channels, k_1 - 1)` when `state_window = W > 0`. Slot `W - 1` holds the last `k - 1` values along the causal axis; slot `j` holds the same for the prefix ending at position `seq_len - W + j`. | required |

## Attributes

Default values (overridable per request):

| Attribute | Default | Description |
| --- | --- | --- |
| `activation` | `"none"` | Activation applied after convolution and bias. Defaults to `none`; `swish` is an alias of SiLU. |
| `ndim` | `1` | Number of spatial dimensions. This implementation supports the contrib 1D mode (`ndim = 1`). |
| `state_window` | `0` | Contrib extension selecting the number of rollback state slots to retain, in the range 0 through 8. Defaults to 0. |

## Type constraints

| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16` |

## Files

- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, 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 + tuning cases
- [`causal-conv-with-state-tiled.wgsl.jinja`](build/webgpu/causal-conv-with-state-tiled.wgsl.jinja)
- [`causal-conv-with-state-vec4.wgsl.jinja`](build/webgpu/causal-conv-with-state-vec4.wgsl.jinja)
- [`causal-conv-with-state.wgsl.jinja`](build/webgpu/causal-conv-with-state.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.

```js
import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/com.microsoft.CausalConvWithState", { version: 1 });
const { outputT, presentStateT } = await kernel({
  inputT: { data: inputTData, shape: [1, 1, 5] },
  weightT: { data: weightTData, shape: [1, 1, 4] },
});
```