concrete.ml.torch.compile

module concrete.ml.torch.compile

torch compilation function.

Global Variables

• MAXIMUM_TLU_BIT_WIDTH

• DEFAULT_P_ERROR_PBS

• OPSET_VERSION_FOR_ONNX_EXPORT

---

function convert_torch_tensor_or_numpy_array_to_numpy_array

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.

---

function compile_torch_model

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.

---

function compile_onnx_model

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.

---

function compile_brevitas_qat_model

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.