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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

Microsoft contrib stateful 1-D causal depthwise convolution. Each channel uses its own (channels, 1, kernel_size) weight over current and past positions, with optional activation and past_state/present_state tensors for incremental decoding. The state_window attribute may retain several rollback states. This package supports ndim = 1, float16 or float32 tensors, and float32 accumulation; spatial ranks 2 and 3 and bfloat16 are unsupported.

See the ONNX Runtime CausalConvWithState contrib-operator spec for the reference semantics.

Inputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`inputT`	`input`	`T`	`3`	—	Input with shape `(batch, channels, length)`, or `(batch, length, channels)` when `channels_last` is 1.	required
`weightT`	`weight`	`T`	`3`	—	Depthwise convolution kernel with shape `(channels, 1, kernel_size)` for the supported 1-D mode.	required
`biasT`	`bias`	`T`	`1`	—	Optional per-channel bias with shape `(channels,)`.	optional
`pastStateT`	`past_state`	`T`	derived	—	Carry state from the previous step; shape `(batch_size, channels, (kernel_size - 1) * dilation)`, or `(W, batch_size, channels, (kernel_size - 1) * dilation)` when `state_window = W > 0`, in which case only slot `W - 1` is read. If absent, the left-side padding is zero. With `channels_last`=1, the length and channel axes are swapped.	optional

Outputs

Name	Upstream name	Logical dtype	Rank	Shape	Description	Presence
`outputT`	`output`	`T`	`3`	same as `inputT`	Convolution output with the same shape as `input`.	required
`presentStateT`	`present_state`	`T`	derived	derived	Updated carry state; shape `(batch_size, channels, (kernel_size - 1) * dilation)`, or `(W, batch_size, channels, (kernel_size - 1) * dilation)` when `state_window = W > 0`. Slot `W - 1` holds the last `(kernel_size - 1) * dilation` values along the causal axis; slot `j` holds the same values for the prefix ending at position `sequence_length - W + j`. With `channels_last`=1, the length and channel axes are swapped.	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.
`channels_last`	`0`	Activation and state layout: 0 selects `(batch, channels, length)`; 1 selects `(batch, length, channels)`.
`dilation`	`1`	Positive integer spacing between taps; the carry state holds (`kernel_size` - 1) * dilation raw samples.
`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 — kernel metadata (id, digests, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark cases
causal-conv-with-state-tiled.wgsl.jinja
causal-conv-with-state-vec4.wgsl.jinja
causal-conv-with-state.wgsl.jinja

Use with `@huggingface/kernels`

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.

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] },
});