> 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.torch.compile.md).

# concrete.ml.torch.compile

[![](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/torch/compile.py#L0)

## module `concrete.ml.torch.compile`

torch compilation function.

### **Global Variables**

* **MAXIMUM\_TLU\_BIT\_WIDTH**
* **DEFAULT\_P\_ERROR\_PBS**
* **OPSET\_VERSION\_FOR\_ONNX\_EXPORT**

***

[![](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/torch/compile.py#L25)

### function `convert_torch_tensor_or_numpy_array_to_numpy_array`

```python
convert_torch_tensor_or_numpy_array_to_numpy_array(
    torch_tensor_or_numpy_array: Union[Tensor, ndarray]
) → ndarray
```

Convert a torch tensor or a numpy array to a numpy array.

**Args:**

* **`torch_tensor_or_numpy_array`** (Tensor): the value that is either a torch tensor or a numpy array.

**Returns:**

* **`numpy.ndarray`**: the value converted to a numpy array.

***

[![](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/torch/compile.py#L126)

### function `compile_torch_model`

```python
compile_torch_model(
    torch_model: Module,
    torch_inputset: Union[Tensor, ndarray, Tuple[Union[Tensor, ndarray], ]],
    import_qat: bool = False,
    configuration: Optional[Configuration] = None,
    compilation_artifacts: Optional[DebugArtifacts] = None,
    show_mlir: bool = False,
    n_bits=8,
    use_virtual_lib: bool = False,
    p_error: Optional[float] = 6.3342483999973e-05
) → QuantizedModule
```

Compile a torch module into an FHE equivalent.

Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs and finally compile it with Concrete-Numpy

**Args:**

* **`torch_model`** (torch.nn.Module): the model to quantize
* **`torch_inputset`** (Dataset): the inputset, can contain either torch tensors or numpy.ndarray, only datasets with a single input are supported for now.
* **`import_qat`** (bool): Set to True to import a network that contains quantizers and was trained using quantization aware training
* **`configuration`** (Configuration): Configuration object to use during compilation
* **`compilation_artifacts`** (DebugArtifacts): Artifacts object to fill during compilation
* **`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
* **`n_bits`**: the number of bits for the quantization
* **`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:**

* **`QuantizedModule`**: The resulting compiled QuantizedModule.

***

[![](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/torch/compile.py#L175)

### function `compile_onnx_model`

```python
compile_onnx_model(
    onnx_model: ModelProto,
    torch_inputset: Union[Tensor, ndarray, Tuple[Union[Tensor, ndarray], ]],
    import_qat: bool = False,
    configuration: Optional[Configuration] = None,
    compilation_artifacts: Optional[DebugArtifacts] = None,
    show_mlir: bool = False,
    n_bits=8,
    use_virtual_lib: bool = False,
    p_error: Optional[float] = 6.3342483999973e-05
) → QuantizedModule
```

Compile a torch module into an FHE equivalent.

Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs and finally compile it with Concrete-Numpy

**Args:**

* **`onnx_model`** (onnx.ModelProto): the model to quantize
* **`torch_inputset`** (Dataset): the inputset, can contain either torch tensors or numpy.ndarray, only datasets with a single input are supported for now.
* **`import_qat`** (bool): Flag to signal that the network being imported contains quantizers in in its computation graph and that Concrete ML should not requantize it.
* **`configuration`** (Configuration): Configuration object to use during compilation
* **`compilation_artifacts`** (DebugArtifacts): Artifacts object to fill during compilation
* **`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
* **`n_bits`**: the number of bits for the quantization
* **`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:**

* **`QuantizedModule`**: The resulting compiled QuantizedModule.

***

[![](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/torch/compile.py#L232)

### function `compile_brevitas_qat_model`

```python
compile_brevitas_qat_model(
    torch_model: Module,
    torch_inputset: Union[Tensor, ndarray, Tuple[Union[Tensor, ndarray], ]],
    n_bits: Union[int, dict],
    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,
    output_onnx_file: Optional[str] = None
) → QuantizedModule
```

Compile a Brevitas Quantization Aware Training model.

The torch\_model parameter is a subclass of torch.nn.Module that uses quantized operations from brevitas.qnn. The model is trained before calling this function. This function compiles the trained model to FHE.

**Args:**

* **`torch_model`** (torch.nn.Module): the model to quantize
* **`torch_inputset`** (Dataset): the inputset, can contain either torch tensors or numpy.ndarray, only datasets with a single input are supported for now.
* **`n_bits`** (Union\[int,dict]): the number of bits for the quantization
* **`configuration`** (Configuration): Configuration object to use during compilation
* **`compilation_artifacts`** (DebugArtifacts): Artifacts object to fill during compilation
* **`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
* **`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
* **`output_onnx_file`** (str): temporary file to store ONNX model. If None a temporary file is generated

**Returns:**

* **`QuantizedModule`**: The resulting compiled QuantizedModule.


---

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