---
title: "QLinear"
sidebar_label: "QLinear"
description: "The clika_runtime.nn QLinear class."
---

<!-- Generated by tools/api_reference/generate_api_docs.py. Do not edit. -->

QLinear(in_features, out_features, bias=True, device=None, dtype=None, *, activation=None)

    Static-quant linear: quantized weight AND quantized activations; the
    forward returns floating point, or a quantized tensor once an output
    requant is bound with :meth:`set_output_quantization`.

    Args:
        in_features: the size of each input sample.
        out_features: the size of each output sample.
        bias: declare the bias slot (an integer bias adds in the integer
            accumulator; a floating-point bias in the real domain).
        device: where the layer's tensors live; ``None`` declares on the
            cpu.
        dtype: the layer's logical dtype; ``None`` adopts the bound
            payload's.
        activation: a fused epilogue applied to every output (a name such
            as ``"relu"`` or an :class:`~clika_runtime.Activation` value);
            ``None`` for none.
    

## `__init__`

```python
__init__(self, in_features: 'int', out_features: 'int', bias: 'bool' = True, device: 'Device | str | None' = None, dtype: 'DtypeLike | None' = None, *, activation: 'ActivationLike' = None) -> 'None'
```

Initialize self.  See help(type(self)) for accurate signature.

## `extra_repr`

```python
extra_repr(self) -> 'str'
```

extra_repr() -> str

One line of per-class detail for :meth:`__repr__`; a layer prints
its geometry here (``in_features=64, out_features=256``).

## `forward`

```python
forward(self, input: 'QTensor') -> 'Tensor'
```

forward(input) -> Tensor

Quantized ``[*, in_features]`` activations to ``[*, out_features]``
(floating point, or quantized once an output requant is bound).

## `from_weights`

```python
from_weightsfrom_weights(weight, bias=None, *, activation=None) -> QLinear
```

from_weights(weight, bias=None, *, activation=None) -> QLinear

Construct FROM a quantized weight: geometry, dtype, and device read
off the payload; an optional ``bias`` binds beside it.

## `set_output_quantization`

```python
set_output_quantization(self, scale: 'Tensor', zero_point: 'Tensor | None' = None, *, quant_axis: 'int' = -1, out_dtype: 'DtypeLike | None' = None) -> 'None'
```

set_output_quantization(scale, zero_point=None, *, quant_axis=-1, out_dtype=None) -> None

Bind the output requant: the forward then returns a quantized
tensor at the bound scheme. Never calling it keeps the
floating-point output path.

## `set_weights`

```python
set_weights(self, weight: 'QTensor', bias: 'Tensor | None' = None) -> 'None'
```

set_weights(weight, bias=None) -> None

Bind the quantized weight and the optional bias; a dense weight is
refused.