Struct QuantizePerChannelPlan 

pub struct QuantizePerChannelPlan<TIn: Element, TOut: IntElement> { /* private fields */ }

quantize_per_channel forward plan.

q[..., c, ...] = clamp(round(x[..., c, ...] / scale[c]) + zero_point[c], q_min, q_max). Per-axis quantization (PyTorch torch.quantize_per_channel).

When to use: post-training quantization of conv / linear weights along the output-channel axis. For activations use QuantizePerTokenPlan; for whole- tensor scale use QuantizePerTensorPlan. Pair with QuantizePerChannelBackwardPlan for STE.

Dtypes: input FP {f32, f64, f16, bf16} × output int {s8, u8}. scale[] is input dtype; zero_point[] is i32. Sub-byte (s4 / u4) deferred.

Shape limits: rank-4 contiguous (caller pads lower-rank tensors with 1’s); axis ∈ [0, 4); per-channel vectors have length shape[axis]. q_max ≥ q_min. Strided per-channel is deferred.

Workspace: none.

Precision guarantee: deterministic, bit-stable on same hardware. Round-ties-even.

Implementations

impl<TIn: Element, TOut: IntElement> QuantizePerChannelPlan<TIn, TOut>

pub fn select( _stream: &Stream, desc: &QuantizePerChannelDescriptor, _pref: PlanPreference, ) -> Result<Self>

Pick a kernel.

pub fn can_implement( &self, args: &QuantizePerChannelArgs<'_, TIn, TOut>, ) -> Result<()>

Validate args.

pub fn workspace_size(&self) -> usize

Workspace bytes.

pub fn sku(&self) -> KernelSku

Identity.

pub fn precision_guarantee(&self) -> PrecisionGuarantee

Numerical guarantees.

pub fn run( &self, stream: &Stream, _workspace: Workspace<'_>, args: QuantizePerChannelArgs<'_, TIn, TOut>, ) -> Result<()>

Launch.