> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/0.4/developer-guide/api/concrete.ml.quantization.quantized_module.md).

# concrete.ml.quantization.quantized\_module

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L0)

## module `concrete.ml.quantization.quantized_module`

QuantizedModule API.

### **Global Variables**

* **DEFAULT\_P\_ERROR\_PBS**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L17)

### class `QuantizedModule`

Inference for a quantized model.

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L27)

#### method `__init__`

```python
__init__(
    ordered_module_input_names: Iterable[str] = None,
    ordered_module_output_names: Iterable[str] = None,
    quant_layers_dict: Dict[str, Tuple[Tuple[str, ], QuantizedOp]] = None
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property is\_compiled**

Return the compiled status of the module.

**Returns:**

* **`bool`**: the compiled status of the module.

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`_onnx_model`** (onnx.ModelProto): the ONNX model

***

**property post\_processing\_params**

Get the post-processing parameters.

**Returns:**

* **`Dict[str, Any]`**: the post-processing parameters

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L301)

#### method `compile`

```python
compile(
    q_inputs: Union[Tuple[ndarray, ], ndarray],
    configuration: Optional[Configuration] = None,
    compilation_artifacts: Optional[DebugArtifacts] = None,
    show_mlir: bool = False,
    use_virtual_lib: bool = False,
    p_error: Optional[float] = 6.3342483999973e-05
) → Circuit
```

Compile the forward function of the module.

**Args:**

* **`q_inputs`** (Union\[Tuple\[numpy.ndarray, ...], numpy.ndarray]): Needed for tracing and building the boundaries.
* **`configuration`** (Optional\[Configuration]): Configuration object to use during compilation
* **`compilation_artifacts`** (Optional\[DebugArtifacts]): Artifacts object to fill during
* **`show_mlir`** (bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo. Defaults to False.
* **`use_virtual_lib`** (bool): set to use the so called virtual lib simulating FHE computation. Defaults to False.
* **`p_error`** (Optional\[float]): probability of error of a PBS.

**Returns:**

* **`Circuit`**: the compiled Circuit.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L269)

#### method `dequantize_output`

```python
dequantize_output(qvalues: ndarray) → ndarray
```

Take the last layer q\_out and use its dequant function.

**Args:**

* **`qvalues`** (numpy.ndarray): Quantized values of the last layer.

**Returns:**

* **`numpy.ndarray`**: Dequantized values of the last layer.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L156)

#### method `forward`

```python
forward(*qvalues: ndarray) → ndarray
```

Forward pass with numpy function only.

**Args:**

* **`*qvalues (numpy.ndarray)`**: numpy.array containing the quantized values.

**Returns:**

* **`(numpy.ndarray)`**: Predictions of the quantized model

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L226)

#### method `forward_and_dequant`

```python
forward_and_dequant(*q_x: ndarray) → ndarray
```

Forward pass with numpy function only plus dequantization.

**Args:**

* **`*q_x (numpy.ndarray)`**: numpy.ndarray containing the quantized input values. Requires the input dtype to be uint8.

**Returns:**

* **`(numpy.ndarray)`**: Predictions of the quantized model

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L105)

#### method `post_processing`

```python
post_processing(qvalues: ndarray) → ndarray
```

Post-processing of the quantized output.

**Args:**

* **`qvalues`** (numpy.ndarray): numpy.ndarray containing the quantized input values.

**Returns:**

* **`(numpy.ndarray)`**: Predictions of the quantized model

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L239)

#### method `quantize_input`

```python
quantize_input(*values: ndarray) → Union[ndarray, Tuple[ndarray, ]]
```

Take the inputs in fp32 and quantize it using the learned quantization parameters.

**Args:**

* **`*values (numpy.ndarray)`**: Floating point values.

**Returns:**

* **`Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]`**: Quantized (numpy.uint32) values.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/quantization/quantized_module.py#L286)

#### method `set_inputs_quantization_parameters`

```python
set_inputs_quantization_parameters(*input_q_params: UniformQuantizer)
```

Set the quantization parameters for the module's inputs.

**Args:**

* **`*input_q_params (UniformQuantizer)`**: The quantizer(s) for the module.


---

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