module concrete.ml.common.check_inputs

Check and conversion tools.

Utils that are used to check (including convert) some data types which are compatible with scikit-learn to numpy types.

function check_array_and_assert

check_array_and_assert(X)

sklearn.utils.check_array with an assert.

Equivalent of sklearn.utils.check_array, with a final assert that the type is one which is supported by Concrete-ML.

Args:

• X (object): Input object to check / convert

Returns: The converted and validated array

function check_X_y_and_assert

check_X_y_and_assert(X, y, *args, **kwargs)

sklearn.utils.check_X_y with an assert.

Equivalent of sklearn.utils.check_X_y, with a final assert that the type is one which is supported by Concrete-ML.

Args:

• X (ndarray, list, sparse matrix): Input data

• y (ndarray, list, sparse matrix): Labels

• *args: The arguments to pass to check_X_y

• **kwargs: The keyword arguments to pass to check_X_y

Returns: The converted and validated arrays

module concrete.ml.common

Module for shared data structures and code.

Global Variables

• debugging

• check_inputs

• utils

module concrete.ml.common.debugging.custom_assert

Provide some variants of assert.

function assert_true

assert_true(
    condition: bool,
    on_error_msg: str = '',
    error_type: Type[Exception] = <class 'AssertionError'>
)

Provide a custom assert to check that the condition is True.

Args:

• condition (bool): the condition. If False, raise AssertionError

• on_error_msg (str): optional message for precising the error, in case of error

• error_type (Type[Exception]): the type of error to raise, if condition is not fulfilled. Default to AssertionError

function assert_false

assert_false(
    condition: bool,
    on_error_msg: str = '',
    error_type: Type[Exception] = <class 'AssertionError'>
)

Provide a custom assert to check that the condition is False.

Args:

• condition (bool): the condition. If True, raise AssertionError

• on_error_msg (str): optional message for precising the error, in case of error

• error_type (Type[Exception]): the type of error to raise, if condition is not fulfilled. Default to AssertionError

function assert_not_reached

assert_not_reached(
    on_error_msg: str,
    error_type: Type[Exception] = <class 'AssertionError'>
)

Provide a custom assert to check that a piece of code is never reached.

Args:

• on_error_msg (str): message for precising the error

• error_type (Type[Exception]): the type of error to raise, if condition is not fulfilled. Default to AssertionError

module concrete.ml.common.debugging

Module for debugging.

Global Variables

• custom_assert

module concrete.ml.onnx.convert

ONNX conversion related code.

Global Variables

• IMPLEMENTED_ONNX_OPS

• OPSET_VERSION_FOR_ONNX_EXPORT

function get_equivalent_numpy_forward_and_onnx_model

get_equivalent_numpy_forward_and_onnx_model(
    torch_module: Module,
    dummy_input: Union[Tensor, Tuple[Tensor, ]],
    output_onnx_file: Optional[Path, str] = None
) → Tuple[Callable[, Tuple[ndarray, ]], GraphProto]

Get the numpy equivalent forward of the provided torch Module.

Args:

• torch_module (torch.nn.Module): the torch Module for which to get the equivalent numpy forward.

• dummy_input (Union[torch.Tensor, Tuple[torch.Tensor, ...]]): dummy inputs for ONNX export.

• output_onnx_file (Optional[Union[Path, str]]): Path to save the ONNX file to. Will use a temp file if not provided. Defaults to None.

Returns:

• Tuple[Callable[..., Tuple[numpy.ndarray, ...]], onnx.GraphProto]: The function that will execute the equivalent numpy code to the passed torch_module and the generated ONNX model.

function get_equivalent_numpy_forward

get_equivalent_numpy_forward(
    onnx_model: ModelProto,
    check_model: bool = True
) → Callable[, Tuple[ndarray, ]]

Get the numpy equivalent forward of the provided ONNX model.

Args:

• onnx_model (onnx.ModelProto): the ONNX model for which to get the equivalent numpy forward.

• check_model (bool): set to True to run the onnx checker on the model. Defaults to True.

Raises:

• ValueError: Raised if there is an unsupported ONNX operator required to convert the torch model to numpy.

Returns:

• Callable[..., Tuple[numpy.ndarray, ...]]: The function that will execute the equivalent numpy function.

module concrete.ml.onnx.onnx_impl_utils

Utility functions for onnx operator implementations.

function numpy_onnx_pad

Pad a tensor according to ONNX spec, using an optional custom pad value.

Args:

• x (numpy.ndarray): input tensor to pad

• pads (List[int]): padding values according to ONNX spec

• pad_value (Optional[Union[float, int]]): value used to fill in padding, default 0

• int_only (bool): set to True to generate integer only code with Concrete-Numpy

Returns:

• res (numpy.ndarray): the input tensor with padding applied

function compute_conv_output_dims

Compute the output shape of a pool or conv operation.

See https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html for details on the computation of the output shape.

Args:

• input_shape (Tuple[int, ...]): shape of the input to be padded as N x C x H x W

• kernel_shape (Tuple[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)

• pads (Tuple[int, ...]): padding values following ONNX spec: dim1_start, dim2_start, .. dimN_start, dim1_end, dim2_end, ... dimN_end where in the 2-d case dim1 is H, dim2 is W

• strides (Tuple[int, ...]): strides for each dimension

• ceil_mode (int): set to 1 to use the ceil function to compute the output shape, as described in the PyTorch doc

Returns:

• res (Tuple[int, ...]): shape of the output of a conv or pool operator with given parameters

function compute_onnx_pool_padding

Compute any additional padding needed to compute pooling layers.

The ONNX standard uses ceil_mode=1 to match tensorflow style pooling output computation. In this setting, the kernel can be placed at a valid position even though it contains values outside of the input shape including padding. The ceil_mode parameter controls whether this mode is enabled. If the mode is not enabled, the output shape follows PyTorch rules.

Args:

• input_shape (Tuple[int, ...]): shape of the input to be padded as N x C x H x W

• kernel_shape (Tuple[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)

• pads (Tuple[int, ...]): padding values following ONNX spec: dim1_start, dim2_start, .. dimN_start, dim1_end, dim2_end, ... dimN_end where in the 2-d case dim1 is H, dim2 is W

• strides (Tuple[int, ...]): strides for each dimension

• ceil_mode (int): set to 1 to use the ceil function to compute the output shape, as described in the PyTorch doc

Returns:

• res (Tuple[int, ...]): shape of the output of a conv or pool operator with given parameters

function onnx_avgpool_compute_norm_const

Compute the average pooling normalization constant.

This constant can be a tensor of the same shape as the input or a scalar.

Args:

• input_shape (Tuple[int, ...]): shape of the input to be padded as N x C x H x W

• kernel_shape (Tuple[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)

• pads (Tuple[int, ...]): padding values following ONNX spec: dim1_start, dim2_start, .. dimN_start, dim1_end, dim2_end, ... dimN_end where in the 2-d case dim1 is H, dim2 is W

• strides (Tuple[int, ...]): strides for each dimension

• ceil_mode (int): set to 1 to use the ceil function to compute the output shape, as described in the PyTorch doc

Returns:

• res (float): tensor or scalar, corresponding to normalization factors to apply for the average pool computation for each valid kernel position

module concrete.ml.deployment.fhe_client_server

APIs for FHE deployment.

Global Variables

• CML_VERSION

• AVAILABLE_MODEL

class FHEModelServer

Server API to load and run the FHE circuit.

method __init__

Initialize the FHE API.

Args:

• path_dir (str): the path to the directory where the circuit is saved

method load

Load the circuit.

method run

Run the model on the server over encrypted data.

Args:

• serialized_encrypted_quantized_data (cnp.PublicArguments): the encrypted, quantized and serialized data

• serialized_evaluation_keys (cnp.EvaluationKeys): the serialized evaluation keys

Returns:

• cnp.PublicResult: the result of the model

class FHEModelDev

Dev API to save the model and then load and run the FHE circuit.

method __init__

Initialize the FHE API.

Args:

• path_dir (str): the path to the directory where the circuit is saved

• model (Any): the model to use for the FHE API

method save

Export all needed artifacts for the client and server.

Raises:

• Exception: path_dir is not empty

class FHEModelClient

Client API to encrypt and decrypt FHE data.

method __init__

Initialize the FHE API.

Args:

• path_dir (str): the path to the directory where the circuit is saved

• key_dir (str): the path to the directory where the keys are stored

method deserialize_decrypt

Deserialize and decrypt the values.

Args:

• serialized_encrypted_quantized_result (cnp.PublicArguments): the serialized, encrypted and quantized result

Returns:

• numpy.ndarray: the decrypted and deserialized values

method deserialize_decrypt_dequantize

Deserialize, decrypt and dequantize the values.

Args:

• serialized_encrypted_quantized_result (cnp.PublicArguments): the serialized, encrypted and quantized result

Returns:

• numpy.ndarray: the decrypted (dequantized) values

method generate_private_and_evaluation_keys

Generate the private and evaluation keys.

Args:

• force (bool): if True, regenerate the keys even if they already exist

method get_serialized_evaluation_keys

Get the serialized evaluation keys.

Returns:

• cnp.EvaluationKeys: the evaluation keys

method load

Load the quantizers along with the FHE specs.

method quantize_encrypt_serialize

Quantize, encrypt and serialize the values.

Args:

• x (numpy.ndarray): the values to quantize, encrypt and serialize

Returns:

• cnp.PublicArguments: the quantized, encrypted and serialized values

module concrete.ml.onnx.onnx_model_manipulations

Some code to manipulate models.

function simplify_onnx_model

Simplify an ONNX model, removes unused Constant nodes and Identity nodes.

Args:

• onnx_model (onnx.ModelProto): the model to simplify.

function remove_unused_constant_nodes

Remove unused Constant nodes in the provided onnx model.

Args:

• onnx_model (onnx.ModelProto): the model for which we want to remove unused Constant nodes.

function remove_identity_nodes

Remove identity nodes from a model.

Args:

• onnx_model (onnx.ModelProto): the model for which we want to remove Identity nodes.

function keep_following_outputs_discard_others

Keep the outputs given in outputs_to_keep and remove the others from the model.

Args:

• onnx_model (onnx.ModelProto): the ONNX model to modify.

• outputs_to_keep (Iterable[str]): the outputs to keep by name.

function remove_node_types

Remove unnecessary nodes from the ONNX graph.

Args:

• onnx_model (onnx.ModelProto): The ONNX model to modify.

• op_types_to_remove (List[str]): The node types to remove from the graph.

Raises:

• ValueError: Wrong replacement by an Identity node.

function clean_graph_after_node_name

Clean the graph of the onnx model by removing nodes after the given node name.

Args:

• onnx_model (onnx.ModelProto): The onnx model.

• node_name (str): The node's name whose following nodes will be removed.

• fail_if_not_found (bool): If true, abort if the node name is not found

Raises:

• ValueError: if the node name is not found and if fail_if_not_found is set

function clean_graph_after_node_op_type

Clean the graph of the onnx model by removing nodes after the given node type.

Args:

• onnx_model (onnx.ModelProto): The onnx model.

• node_op_type (str): The node's op_type whose following nodes will be removed.

• fail_if_not_found (bool): If true, abort if the node op_type is not found

Raises:

• ValueError: if the node op_type is not found and if fail_if_not_found is set

Modules

• concrete.ml.common: Module for shared data structures and code.

• concrete.ml.common.check_inputs: Check and conversion tools.

• concrete.ml.common.debugging: Module for debugging.

• concrete.ml.common.debugging.custom_assert: Provide some variants of assert.

• concrete.ml.common.utils: Utils that can be re-used by other pieces of code in the module.

• concrete.ml.deployment: Module for deployment of the FHE model.

• concrete.ml.deployment.fhe_client_server: APIs for FHE deployment.

• concrete.ml.onnx: ONNX module.

• concrete.ml.onnx.convert: ONNX conversion related code.

• concrete.ml.onnx.onnx_impl_utils: Utility functions for onnx operator implementations.

• concrete.ml.onnx.onnx_model_manipulations: Some code to manipulate models.

• concrete.ml.onnx.onnx_utils: Utils to interpret an ONNX model with numpy.

• concrete.ml.onnx.ops_impl: ONNX ops implementation in python + numpy.

• concrete.ml.pytest: Module which is used to contain common functions for pytest.

• concrete.ml.pytest.torch_models: Torch modules for our pytests.

• concrete.ml.pytest.utils: Common functions or lists for test files, which can't be put in fixtures.

• concrete.ml.quantization: Modules for quantization.

• concrete.ml.quantization.base_quantized_op: Base Quantized Op class that implements quantization for a float numpy op.

• concrete.ml.quantization.post_training: Post Training Quantization methods.

• concrete.ml.quantization.quantized_module: QuantizedModule API.

• concrete.ml.quantization.quantized_ops: Quantized versions of the ONNX operators for post training quantization.

• concrete.ml.quantization.quantizers: Quantization utilities for a numpy array/tensor.

• concrete.ml.sklearn: Import sklearn models.

• concrete.ml.sklearn.base: Module that contains base classes for our libraries estimators.

• concrete.ml.sklearn.glm: Implement sklearn's Generalized Linear Models (GLM).

• concrete.ml.sklearn.linear_model: Implement sklearn linear model.

• concrete.ml.sklearn.protocols: Protocols.

• concrete.ml.sklearn.qnn: Scikit-learn interface for concrete quantized neural networks.

• concrete.ml.sklearn.rf: Implements RandomForest models.

• concrete.ml.sklearn.svm: Implement Support Vector Machine.

• concrete.ml.sklearn.torch_modules: Implement torch module.

• concrete.ml.sklearn.tree: Implement the sklearn tree models.

• concrete.ml.sklearn.tree_to_numpy: Implements the conversion of a tree model to a numpy function.

• concrete.ml.sklearn.xgb: Implements XGBoost models.

• concrete.ml.torch: Modules for torch to numpy conversion.

• concrete.ml.torch.compile: torch compilation function.

• concrete.ml.torch.numpy_module: A torch to numpy module.

• concrete.ml.version: File to manage the version of the package.

Classes

• fhe_client_server.FHEModelClient: Client API to encrypt and decrypt FHE data.

• fhe_client_server.FHEModelDev: Dev API to save the model and then load and run the FHE circuit.

• fhe_client_server.FHEModelServer: Server API to load and run the FHE circuit.

• ops_impl.ONNXMixedFunction: A mixed quantized-raw valued onnx function.

• torch_models.BranchingGemmModule: Torch model with some branching and skip connections.

• torch_models.BranchingModule: Torch model with some branching and skip connections.

• torch_models.CNN: Torch CNN model for the tests.

• torch_models.CNNGrouped: Torch CNN model with grouped convolution for compile torch tests.

• torch_models.CNNInvalid: Torch CNN model for the tests.

• torch_models.CNNMaxPool: Torch CNN model for the tests with a max pool.

• torch_models.CNNOther: Torch CNN model for the tests.

• torch_models.FC: Torch model for the tests.

• torch_models.FCSeq: Torch model that should generate MatMul->Add ONNX patterns.

• torch_models.FCSeqAddBiasVec: Torch model that should generate MatMul->Add ONNX patterns.

• torch_models.FCSmall: Torch model for the tests.

• torch_models.MultiInputNN: Torch model to test multiple inputs forward.

• torch_models.MultiOpOnSingleInputConvNN: Network that applies two quantized operations on a single input.

• torch_models.NetWithConcatUnsqueeze: Torch model to test the concat and unsqueeze operators.

• torch_models.NetWithLoops: Torch model, where we reuse some elements in a loop.

• torch_models.QATTestModule: Torch model that implements a simple non-uniform quantizer.

• torch_models.SimpleQAT: Torch model implements a step function that needs Greater, Cast and Where.

• torch_models.SingleMixNet: Torch model that with a single conv layer that produces the output, e.g. a blur filter.

• torch_models.StepActivationModule: Torch model implements a step function that needs Greater, Cast and Where.

• torch_models.TinyCNN: A very small CNN.

• torch_models.TinyQATCNN: A very small QAT CNN to classify the sklearn digits dataset.

• torch_models.TorchSum: Torch model to test the ReduceSum ONNX operator in a leveled circuit.

• torch_models.TorchSumMod: Torch model to test the ReduceSum ONNX operator in a circuit containing a PBS.

• torch_models.UnivariateModule: Torch model that calls univariate and shape functions of torch.

• base_quantized_op.QuantizedMixingOp: An operator that mixes (adds or multiplies) together encrypted inputs.

• base_quantized_op.QuantizedOp: Base class for quantized ONNX ops implemented in numpy.

• base_quantized_op.QuantizedOpUnivariateOfEncrypted: An univariate operator of an encrypted value.

• post_training.ONNXConverter: Base ONNX to Concrete ML computation graph conversion class.

• post_training.PostTrainingAffineQuantization: Post-training Affine Quantization.

• post_training.PostTrainingQATImporter: Converter of Quantization Aware Training networks.

• quantized_module.QuantizedModule: Inference for a quantized model.

• quantized_ops.QuantizedAbs: Quantized Abs op.

• quantized_ops.QuantizedAdd: Quantized Addition operator.

• quantized_ops.QuantizedAvgPool: Quantized Average Pooling op.

• quantized_ops.QuantizedBatchNormalization: Quantized Batch normalization with encrypted input and in-the-clear normalization params.

• quantized_ops.QuantizedBrevitasQuant: Brevitas uniform quantization with encrypted input.

• quantized_ops.QuantizedCast: Cast the input to the required data type.

• quantized_ops.QuantizedCelu: Quantized Celu op.

• quantized_ops.QuantizedClip: Quantized clip op.

• quantized_ops.QuantizedConcat: Concatenate operator.

• quantized_ops.QuantizedConv: Quantized Conv op.

• quantized_ops.QuantizedDiv: Div operator /.

• quantized_ops.QuantizedElu: Quantized Elu op.

• quantized_ops.QuantizedErf: Quantized erf op.

• quantized_ops.QuantizedExp: Quantized Exp op.

• quantized_ops.QuantizedFlatten: Quantized flatten for encrypted inputs.

• quantized_ops.QuantizedFloor: Quantized Floor op.

• quantized_ops.QuantizedGemm: Quantized Gemm op.

• quantized_ops.QuantizedGreater: Comparison operator >.

• quantized_ops.QuantizedGreaterOrEqual: Comparison operator >=.

• quantized_ops.QuantizedHardSigmoid: Quantized HardSigmoid op.

• quantized_ops.QuantizedHardSwish: Quantized Hardswish op.

• quantized_ops.QuantizedIdentity: Quantized Identity op.

• quantized_ops.QuantizedLeakyRelu: Quantized LeakyRelu op.

• quantized_ops.QuantizedLess: Comparison operator <.

• quantized_ops.QuantizedLessOrEqual: Comparison operator <=.

• quantized_ops.QuantizedLog: Quantized Log op.

• quantized_ops.QuantizedMatMul: Quantized MatMul op.

• quantized_ops.QuantizedMax: Quantized Max op.

• quantized_ops.QuantizedMaxPool: Quantized Max Pooling op.

• quantized_ops.QuantizedMin: Quantized Min op.

• quantized_ops.QuantizedMul: Multiplication operator.

• quantized_ops.QuantizedNeg: Quantized Neg op.

• quantized_ops.QuantizedNot: Quantized Not op.

• quantized_ops.QuantizedOr: Or operator ||.

• quantized_ops.QuantizedPRelu: Quantized PRelu op.

• quantized_ops.QuantizedPad: Quantized Padding op.

• quantized_ops.QuantizedPow: Quantized pow op.

• quantized_ops.QuantizedReduceSum: ReduceSum with encrypted input.

• quantized_ops.QuantizedRelu: Quantized Relu op.

• quantized_ops.QuantizedReshape: Quantized Reshape op.

• quantized_ops.QuantizedRound: Quantized round op.

• quantized_ops.QuantizedSelu: Quantized Selu op.

• quantized_ops.QuantizedSigmoid: Quantized sigmoid op.

• quantized_ops.QuantizedSign: Quantized Neg op.

• quantized_ops.QuantizedSoftplus: Quantized Softplus op.

• quantized_ops.QuantizedSub: Subtraction operator.

• quantized_ops.QuantizedTanh: Quantized Tanh op.

• quantized_ops.QuantizedTranspose: Transpose operator for quantized inputs.

• quantized_ops.QuantizedUnsqueeze: Unsqueeze operator.

• quantized_ops.QuantizedWhere: Where operator on quantized arrays.

• quantizers.MinMaxQuantizationStats: Calibration set statistics.

• quantizers.QuantizationOptions: Options for quantization.

• quantizers.QuantizedArray: Abstraction of quantized array.

• quantizers.UniformQuantizationParameters: Quantization parameters for uniform quantization.

• quantizers.UniformQuantizer: Uniform quantizer.

• base.BaseTreeClassifierMixin: Mixin class for tree-based classifiers.

• base.BaseTreeEstimatorMixin: Mixin class for tree-based estimators.

• base.BaseTreeRegressorMixin: Mixin class for tree-based regressors.

• base.QuantizedTorchEstimatorMixin: Mixin that provides quantization for a torch module and follows the Estimator API.

• base.SklearnLinearClassifierMixin: A Mixin class for sklearn linear classifiers with FHE.

• base.SklearnLinearModelMixin: A Mixin class for sklearn linear models with FHE.

• glm.GammaRegressor: A Gamma regression model with FHE.

• glm.PoissonRegressor: A Poisson regression model with FHE.

• glm.TweedieRegressor: A Tweedie regression model with FHE.

• linear_model.ElasticNet: An ElasticNet regression model with FHE.

• linear_model.Lasso: A Lasso regression model with FHE.

• linear_model.LinearRegression: A linear regression model with FHE.

• linear_model.LogisticRegression: A logistic regression model with FHE.

• linear_model.Ridge: A Ridge regression model with FHE.

• protocols.ConcreteBaseClassifierProtocol: Concrete classifier protocol.

• protocols.ConcreteBaseEstimatorProtocol: A Concrete Estimator Protocol.

• protocols.ConcreteBaseRegressorProtocol: Concrete regressor protocol.

• protocols.Quantizer: Quantizer Protocol.

• qnn.FixedTypeSkorchNeuralNet: A mixin with a helpful modification to a skorch estimator that fixes the module type.

• qnn.NeuralNetClassifier: Scikit-learn interface for quantized FHE compatible neural networks.

• qnn.NeuralNetRegressor: Scikit-learn interface for quantized FHE compatible neural networks.

• qnn.QuantizedSkorchEstimatorMixin: Mixin class that adds quantization features to Skorch NN estimators.

• qnn.SparseQuantNeuralNetImpl: Sparse Quantized Neural Network classifier.

• rf.RandomForestClassifier: Implements the RandomForest classifier.

• rf.RandomForestRegressor: Implements the RandomForest regressor.

• svm.LinearSVC: A Classification Support Vector Machine (SVM).

• svm.LinearSVR: A Regression Support Vector Machine (SVM).

• tree.DecisionTreeClassifier: Implements the sklearn DecisionTreeClassifier.

• tree.DecisionTreeRegressor: Implements the sklearn DecisionTreeClassifier.

• tree_to_numpy.Task: Task enumerate.

• xgb.XGBClassifier: Implements the XGBoost classifier.

• xgb.XGBRegressor: Implements the XGBoost regressor.

• numpy_module.NumpyModule: General interface to transform a torch.nn.Module to numpy module.

Functions

• check_inputs.check_X_y_and_assert: sklearn.utils.check_X_y with an assert.

• check_inputs.check_array_and_assert: sklearn.utils.check_array with an assert.

• custom_assert.assert_false: Provide a custom assert to check that the condition is False.

• custom_assert.assert_not_reached: Provide a custom assert to check that a piece of code is never reached.

• custom_assert.assert_true: Provide a custom assert to check that the condition is True.

• utils.check_there_is_no_p_error_options_in_configuration: Check the user did not set p_error or global_p_error in configuration.

• utils.generate_proxy_function: Generate a proxy function for a function accepting only *args type arguments.

• utils.get_onnx_opset_version: Return the ONNX opset_version.

• utils.manage_parameters_for_pbs_errors: Return (p_error, global_p_error) that we want to give to Concrete-Numpy and the compiler.

• utils.replace_invalid_arg_name_chars: Sanitize arg_name, replacing invalid chars by _.

• convert.get_equivalent_numpy_forward: Get the numpy equivalent forward of the provided ONNX model.

• convert.get_equivalent_numpy_forward_and_onnx_model: Get the numpy equivalent forward of the provided torch Module.

• onnx_impl_utils.compute_conv_output_dims: Compute the output shape of a pool or conv operation.

• onnx_impl_utils.compute_onnx_pool_padding: Compute any additional padding needed to compute pooling layers.

• onnx_impl_utils.numpy_onnx_pad: Pad a tensor according to ONNX spec, using an optional custom pad value.

• onnx_impl_utils.onnx_avgpool_compute_norm_const: Compute the average pooling normalization constant.

• onnx_model_manipulations.clean_graph_after_node_name: Clean the graph of the onnx model by removing nodes after the given node name.

• onnx_model_manipulations.clean_graph_after_node_op_type: Clean the graph of the onnx model by removing nodes after the given node type.

• onnx_model_manipulations.keep_following_outputs_discard_others: Keep the outputs given in outputs_to_keep and remove the others from the model.

• onnx_model_manipulations.remove_identity_nodes: Remove identity nodes from a model.

• onnx_model_manipulations.remove_node_types: Remove unnecessary nodes from the ONNX graph.

• onnx_model_manipulations.remove_unused_constant_nodes: Remove unused Constant nodes in the provided onnx model.

• onnx_model_manipulations.simplify_onnx_model: Simplify an ONNX model, removes unused Constant nodes and Identity nodes.

• onnx_utils.execute_onnx_with_numpy: Execute the provided ONNX graph on the given inputs.

• onnx_utils.get_attribute: Get the attribute from an ONNX AttributeProto.

• onnx_utils.get_op_type: Construct the qualified type name of the ONNX operator.

• onnx_utils.remove_initializer_from_input: Remove initializers from model inputs.

• ops_impl.cast_to_float: Cast values to floating points.

• ops_impl.numpy_abs: Compute abs in numpy according to ONNX spec.

• ops_impl.numpy_acos: Compute acos in numpy according to ONNX spec.

• ops_impl.numpy_acosh: Compute acosh in numpy according to ONNX spec.

• ops_impl.numpy_add: Compute add in numpy according to ONNX spec.

• ops_impl.numpy_asin: Compute asin in numpy according to ONNX spec.

• ops_impl.numpy_asinh: Compute sinh in numpy according to ONNX spec.

• ops_impl.numpy_atan: Compute atan in numpy according to ONNX spec.

• ops_impl.numpy_atanh: Compute atanh in numpy according to ONNX spec.

• ops_impl.numpy_avgpool: Compute Average Pooling using Torch.

• ops_impl.numpy_batchnorm: Compute the batch normalization of the input tensor.

• ops_impl.numpy_cast: Execute ONNX cast in Numpy.

• ops_impl.numpy_celu: Compute celu in numpy according to ONNX spec.

• ops_impl.numpy_concatenate: Apply concatenate in numpy according to ONNX spec.

• ops_impl.numpy_constant: Return the constant passed as a kwarg.

• ops_impl.numpy_cos: Compute cos in numpy according to ONNX spec.

• ops_impl.numpy_cosh: Compute cosh in numpy according to ONNX spec.

• ops_impl.numpy_div: Compute div in numpy according to ONNX spec.

• ops_impl.numpy_elu: Compute elu in numpy according to ONNX spec.

• ops_impl.numpy_equal: Compute equal in numpy according to ONNX spec.

• ops_impl.numpy_erf: Compute erf in numpy according to ONNX spec.

• ops_impl.numpy_exp: Compute exponential in numpy according to ONNX spec.

• ops_impl.numpy_flatten: Flatten a tensor into a 2d array.

• ops_impl.numpy_floor: Compute Floor in numpy according to ONNX spec.

• ops_impl.numpy_greater: Compute greater in numpy according to ONNX spec.

• ops_impl.numpy_greater_float: Compute greater in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_greater_or_equal: Compute greater or equal in numpy according to ONNX spec.

• ops_impl.numpy_greater_or_equal_float: Compute greater or equal in numpy according to ONNX specs and cast outputs to floats.

• ops_impl.numpy_hardsigmoid: Compute hardsigmoid in numpy according to ONNX spec.

• ops_impl.numpy_hardswish: Compute hardswish in numpy according to ONNX spec.

• ops_impl.numpy_identity: Compute identity in numpy according to ONNX spec.

• ops_impl.numpy_leakyrelu: Compute leakyrelu in numpy according to ONNX spec.

• ops_impl.numpy_less: Compute less in numpy according to ONNX spec.

• ops_impl.numpy_less_float: Compute less in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_less_or_equal: Compute less or equal in numpy according to ONNX spec.

• ops_impl.numpy_less_or_equal_float: Compute less or equal in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_log: Compute log in numpy according to ONNX spec.

• ops_impl.numpy_matmul: Compute matmul in numpy according to ONNX spec.

• ops_impl.numpy_max: Compute Max in numpy according to ONNX spec.

• ops_impl.numpy_maxpool: Compute Max Pooling using Torch.

• ops_impl.numpy_min: Compute Min in numpy according to ONNX spec.

• ops_impl.numpy_mul: Compute mul in numpy according to ONNX spec.

• ops_impl.numpy_neg: Compute Negative in numpy according to ONNX spec.

• ops_impl.numpy_not: Compute not in numpy according to ONNX spec.

• ops_impl.numpy_not_float: Compute not in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_or: Compute or in numpy according to ONNX spec.

• ops_impl.numpy_or_float: Compute or in numpy according to ONNX spec and cast outputs to floats.

• ops_impl.numpy_pow: Compute pow in numpy according to ONNX spec.

• ops_impl.numpy_relu: Compute relu in numpy according to ONNX spec.

• ops_impl.numpy_round: Compute round in numpy according to ONNX spec.

• ops_impl.numpy_selu: Compute selu in numpy according to ONNX spec.

• ops_impl.numpy_sigmoid: Compute sigmoid in numpy according to ONNX spec.

• ops_impl.numpy_sign: Compute Sign in numpy according to ONNX spec.

• ops_impl.numpy_sin: Compute sin in numpy according to ONNX spec.

• ops_impl.numpy_sinh: Compute sinh in numpy according to ONNX spec.

• ops_impl.numpy_softmax: Compute softmax in numpy according to ONNX spec.

• ops_impl.numpy_softplus: Compute softplus in numpy according to ONNX spec.

• ops_impl.numpy_sub: Compute sub in numpy according to ONNX spec.

• ops_impl.numpy_tan: Compute tan in numpy according to ONNX spec.

• ops_impl.numpy_tanh: Compute tanh in numpy according to ONNX spec.

• ops_impl.numpy_thresholdedrelu: Compute thresholdedrelu in numpy according to ONNX spec.

• ops_impl.numpy_transpose: Transpose in numpy according to ONNX spec.

• ops_impl.numpy_where: Compute the equivalent of numpy.where.

• ops_impl.numpy_where_body: Compute the equivalent of numpy.where.

• ops_impl.onnx_func_raw_args: Decorate a numpy onnx function to flag the raw/non quantized inputs.

• utils.sanitize_test_and_train_datasets: Sanitize datasets depending on the model type.

• post_training.get_n_bits_dict: Convert the n_bits parameter into a proper dictionary.

• quantizers.fill_from_kwargs: Fill a parameter set structure from kwargs parameters.

• base.get_sklearn_linear_models: Return the list of available linear models in Concrete-ML.

• base.get_sklearn_models: Return the list of available models in Concrete-ML.

• base.get_sklearn_neural_net_models: Return the list of available neural net models in Concrete-ML.

• base.get_sklearn_tree_models: Return the list of available tree models in Concrete-ML.

• tree_to_numpy.tree_to_numpy: Convert the tree inference to a numpy functions using Hummingbird.

• compile.compile_brevitas_qat_model: Compile a Brevitas Quantization Aware Training model.

• compile.compile_onnx_model: Compile a torch module into a FHE equivalent.

• compile.compile_torch_model: Compile a torch module into a FHE equivalent.

• compile.convert_torch_tensor_or_numpy_array_to_numpy_array: Convert a torch tensor or a numpy array to a numpy array.

module concrete.ml.deployment

Module for deployment of the FHE model.

Global Variables

• fhe_client_server

module concrete.ml.pytest

Module which is used to contain common functions for pytest.

Global Variables

• torch_models

• utils

module concrete.ml.pytest.torch_models

Torch modules for our pytests.

class FCSmall

Torch model for the tests.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class FC

Torch model for the tests.

method __init__

__init__(activation_function, input_output=3072)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class CNN

Torch CNN model for the tests.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class CNNMaxPool

Torch CNN model for the tests with a max pool.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class CNNOther

Torch CNN model for the tests.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class CNNInvalid

Torch CNN model for the tests.

method __init__

__init__(activation_function, padding, groups, gather_slice)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class CNNGrouped

Torch CNN model with grouped convolution for compile torch tests.

method __init__

__init__(input_output, activation_function, groups)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class NetWithLoops

Torch model, where we reuse some elements in a loop.

Torch model, where we reuse some elements in a loop in the forward and don't expect the user to define these elements in a particular order.

method __init__

__init__(activation_function, input_output, n_fc_layers)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class MultiInputNN

Torch model to test multiple inputs forward.

method __init__

__init__(input_output, activation_function)

method forward

forward(x, y)

Forward pass.

Args:

• x: the first input of the NN

• y: the second input of the NN

Returns: the output of the NN

class BranchingModule

Torch model with some branching and skip connections.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class BranchingGemmModule

Torch model with some branching and skip connections.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class UnivariateModule

Torch model that calls univariate and shape functions of torch.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class StepActivationModule

Torch model implements a step function that needs Greater, Cast and Where.

method __init__

__init__(input_output, activation_function)

method forward

forward(x)

Forward pass with a quantizer built into the computation graph.

Args:

• x: the input of the NN

Returns: the output of the NN

class NetWithConcatUnsqueeze

Torch model to test the concat and unsqueeze operators.

method __init__

__init__(activation_function, input_output, n_fc_layers)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class MultiOpOnSingleInputConvNN

Network that applies two quantized operations on a single input.

method __init__

__init__()

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class FCSeq

Torch model that should generate MatMul->Add ONNX patterns.

This network generates additions with a constant scalar

method __init__

__init__(input_output, act)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class FCSeqAddBiasVec

Torch model that should generate MatMul->Add ONNX patterns.

This network tests the addition with a constant vector

method __init__

__init__(input_output, act)

method forward

forward(x)

Forward pass.

Args:

• x: the input of the NN

Returns: the output of the NN

class TinyCNN

A very small CNN.

method __init__

__init__(n_classes, act) → None

Create the tiny CNN with two conv layers.

Args:

• n_classes: number of classes

• act: the activation

method forward

forward(x)

Forward the two layers with the chosen activation function.

Args:

• x: the input of the NN

Returns: the output of the NN

class TinyQATCNN

A very small QAT CNN to classify the sklearn digits dataset.

This class also allows pruning to a maximum of 10 active neurons, which should help keep the accumulator bit-width low.

method __init__

__init__(n_classes, n_bits, n_active, signed, narrow) → None

Construct the CNN with a configurable number of classes.

Args:

• n_classes (int): number of outputs of the neural net

• n_bits (int): number of weight and activation bits for quantization

• n_active (int): number of active (non-zero weight) neurons to keep

• signed (bool): whether quantized integer values are signed

• narrow (bool): whether the range of quantized integer values is narrow/symmetric

method forward

forward(x)

Run inference on the tiny CNN, apply the decision layer on the reshaped conv output.

Args:

• x: the input to the NN

Returns: the output of the NN

method test_torch

test_torch(test_loader)

Test the network: measure accuracy on the test set.

Args:

• test_loader: the test loader

Returns:

• res: the number of correctly classified test examples

method toggle_pruning

toggle_pruning(enable)

Enable or remove pruning.

Args:

• enable: if we enable the pruning or not

class SimpleQAT

Torch model implements a step function that needs Greater, Cast and Where.

method __init__

__init__(input_output, activation_function, n_bits=2, disable_bit_check=False)

method forward

forward(x)

Forward pass with a quantizer built into the computation graph.

Args:

• x: the input of the NN

Returns: the output of the NN

class QATTestModule

Torch model that implements a simple non-uniform quantizer.

method __init__

__init__(activation_function)

method forward

forward(x)

Forward pass with a quantizer built into the computation graph.

Args:

• x: the input of the NN

Returns: the output of the NN

class SingleMixNet

Torch model that with a single conv layer that produces the output, e.g. a blur filter.

method __init__

__init__(use_conv, use_qat, inp_size, n_bits)

method forward

forward(x)

Execute the single convolution.

Args:

• x: the input of the NN

Returns: the output of the NN

class TorchSum

Torch model to test the ReduceSum ONNX operator in a leveled circuit.

method __init__

__init__(dim=(0,), keepdim=True)

Initialize the module.

Args:

• dim (Tuple[int]): The axis along which the sum should be executed

• keepdim (bool): If the output should keep the same dimension as the input or not

method forward

forward(x)

Forward pass.

Args:

• x (torch.tensor): The input of the model

Returns:

• torch_sum (torch.tensor): The sum of the input's tensor elements along the given axis

class TorchSumMod

Torch model to test the ReduceSum ONNX operator in a circuit containing a PBS.

method __init__

__init__(dim=(0,), keepdim=True)

Initialize the module.

Args:

• dim (Tuple[int]): The axis along which the sum should be executed

• keepdim (bool): If the output should keep the same dimension as the input or not

method forward

forward(x)

Forward pass.

Args:

• x (torch.tensor): The input of the model

Returns:

• torch_sum (torch.tensor): The sum of the input's tensor elements along the given axis

module concrete.ml.pytest.utils

Common functions or lists for test files, which can't be put in fixtures.

Global Variables

• regressor_models

• classifier_models

• classifiers

• regressors

function sanitize_test_and_train_datasets

sanitize_test_and_train_datasets(model, x, y)

Sanitize datasets depending on the model type.

Args:

• model: the model

• x: the first output of load_data, i.e., the inputs

• y: the second output of load_data, i.e., the labels

Returns: Tuple containing sanitized (model_params, x, y, x_train, y_train, x_test)

module concrete.ml.onnx.onnx_utils

Utils to interpret an ONNX model with numpy.

Global Variables

• ATTR_TYPES

• ATTR_GETTERS

• ONNX_OPS_TO_NUMPY_IMPL

• ONNX_COMPARISON_OPS_TO_NUMPY_IMPL_FLOAT

• ONNX_COMPARISON_OPS_TO_NUMPY_IMPL_BOOL

• ONNX_OPS_TO_NUMPY_IMPL_BOOL

• IMPLEMENTED_ONNX_OPS

function get_attribute

get_attribute(attribute: AttributeProto) → Any

Get the attribute from an ONNX AttributeProto.

Args:

• attribute (onnx.AttributeProto): The attribute to retrieve the value from.

Returns:

• Any: The stored attribute value.

function get_op_type

get_op_type(node)

Construct the qualified type name of the ONNX operator.

Args:

• node (Any): ONNX graph node

Returns:

• result (str): qualified name

function execute_onnx_with_numpy

execute_onnx_with_numpy(graph: GraphProto, *inputs: ndarray) → Tuple[ndarray, ]

Execute the provided ONNX graph on the given inputs.

Args:

• graph (onnx.GraphProto): The ONNX graph to execute.

• *inputs: The inputs of the graph.

Returns:

• Tuple[numpy.ndarray]: The result of the graph's execution.

function remove_initializer_from_input

remove_initializer_from_input(model: ModelProto)

Remove initializers from model inputs.

In some cases, ONNX initializers may appear, erroneously, as graph inputs. This function searches all model inputs and removes those that are initializers.

Args:

• model (onnx.ModelProto): the model to clean

Returns:

• onnx.ModelProto: the cleaned model

module concrete.ml.onnx

ONNX module.

Global Variables

• onnx_impl_utils

• ops_impl

• onnx_utils

• convert

• onnx_model_manipulations

module concrete.ml.onnx.ops_impl

ONNX ops implementation in python + numpy.

function cast_to_float

cast_to_float(inputs)

Cast values to floating points.

Args:

• inputs (Tuple[numpy.ndarray]): The values to consider.

Returns:

• Tuple[numpy.ndarray]: The float values.

function onnx_func_raw_args

onnx_func_raw_args(*args)

Decorate a numpy onnx function to flag the raw/non quantized inputs.

Args:

• *args (tuple[Any]): function argument names

Returns:

• result (ONNXMixedFunction): wrapped numpy function with a list of mixed arguments

function numpy_where_body

numpy_where_body(c: ndarray, t: ndarray, f: Union[ndarray, int]) → ndarray

Compute the equivalent of numpy.where.

This function is not mapped to any ONNX operator (as opposed to numpy_where). It is usable by functions which are mapped to ONNX operators, e.g. numpy_div or numpy_where.

Args:

• c (numpy.ndarray): Condition operand.

• t (numpy.ndarray): True operand.

• f (numpy.ndarray): False operand.

Returns:

• numpy.ndarray: numpy.where(c, t, f)

function numpy_where

numpy_where(c: ndarray, t: ndarray, f: ndarray) → Tuple[ndarray]

Compute the equivalent of numpy.where.

Args:

• c (numpy.ndarray): Condition operand.

• t (numpy.ndarray): True operand.

• f (numpy.ndarray): False operand.

Returns:

• numpy.ndarray: numpy.where(c, t, f)

function numpy_add

numpy_add(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute add in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Add-13

Args:

• a (numpy.ndarray): First operand.

• b (numpy.ndarray): Second operand.

Returns:

• Tuple[numpy.ndarray]: Result, has same element type as two inputs

function numpy_constant

numpy_constant(**kwargs)

Return the constant passed as a kwarg.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Constant-13

Args:

• **kwargs: keyword arguments

Returns:

• Any: The stored constant.

function numpy_matmul

numpy_matmul(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute matmul in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#MatMul-13

Args:

• a (numpy.ndarray): N-dimensional matrix A

• b (numpy.ndarray): N-dimensional matrix B

Returns:

• Tuple[numpy.ndarray]: Matrix multiply results from A * B

function numpy_relu

numpy_relu(x: ndarray) → Tuple[ndarray]

Compute relu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Relu-14

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_sigmoid

numpy_sigmoid(x: ndarray) → Tuple[ndarray]

Compute sigmoid in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sigmoid-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_softmax

numpy_softmax(x, axis=1, keepdims=True)

Compute softmax in numpy according to ONNX spec.

Softmax is currently not supported in FHE.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#softmax-13

Args:

• x (numpy.ndarray): Input tensor

• axis (None, int, tuple of int): Axis or axes along which a softmax's sum is performed. If None, it will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. Default to 1.

• keepdims (bool): If True, the axes which are reduced along the sum are left in the result as dimensions with size one. Default to True.

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_cos

numpy_cos(x: ndarray) → Tuple[ndarray]

Compute cos in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Cos-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_cosh

numpy_cosh(x: ndarray) → Tuple[ndarray]

Compute cosh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Cosh-9

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_sin

numpy_sin(x: ndarray) → Tuple[ndarray]

Compute sin in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sin-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_sinh

numpy_sinh(x: ndarray) → Tuple[ndarray]

Compute sinh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sinh-9

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_tan

numpy_tan(x: ndarray) → Tuple[ndarray]

Compute tan in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Tan-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_tanh

numpy_tanh(x: ndarray) → Tuple[ndarray]

Compute tanh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Tanh-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_acos

numpy_acos(x: ndarray) → Tuple[ndarray]

Compute acos in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Acos-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_acosh

numpy_acosh(x: ndarray) → Tuple[ndarray]

Compute acosh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Acosh-9

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_asin

numpy_asin(x: ndarray) → Tuple[ndarray]

Compute asin in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Asin-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_asinh

numpy_asinh(x: ndarray) → Tuple[ndarray]

Compute sinh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Asinh-9

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_atan

numpy_atan(x: ndarray) → Tuple[ndarray]

Compute atan in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Atan-7

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_atanh

numpy_atanh(x: ndarray) → Tuple[ndarray]

Compute atanh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Atanh-9

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_elu

numpy_elu(x: ndarray, alpha: float = 1) → Tuple[ndarray]

Compute elu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Elu-6

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_selu

numpy_selu(
    x: ndarray,
    alpha: float = 1.6732632423543772,
    gamma: float = 1.0507009873554805
) → Tuple[ndarray]

Compute selu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Selu-6

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

• gamma (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_celu

numpy_celu(x: ndarray, alpha: float = 1) → Tuple[ndarray]

Compute celu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Celu-12

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_leakyrelu

numpy_leakyrelu(x: ndarray, alpha: float = 0.01) → Tuple[ndarray]

Compute leakyrelu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#LeakyRelu-6

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_thresholdedrelu

numpy_thresholdedrelu(x: ndarray, alpha: float = 1) → Tuple[ndarray]

Compute thresholdedrelu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#ThresholdedRelu-10

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_hardsigmoid

numpy_hardsigmoid(
    x: ndarray,
    alpha: float = 0.2,
    beta: float = 0.5
) → Tuple[ndarray]

Compute hardsigmoid in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#HardSigmoid-6

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

• beta (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_softplus

numpy_softplus(x: ndarray) → Tuple[ndarray]

Compute softplus in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Softplus-1

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_abs

numpy_abs(x: ndarray) → Tuple[ndarray]

Compute abs in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Abs-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_div

numpy_div(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute div in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Div-14

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_mul

numpy_mul(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute mul in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Mul-14

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_sub

numpy_sub(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute sub in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sub-14

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_log

numpy_log(x: ndarray) → Tuple[ndarray]

Compute log in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Log-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_erf

numpy_erf(x: ndarray) → Tuple[ndarray]

Compute erf in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Erf-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_hardswish

numpy_hardswish(x: ndarray) → Tuple[ndarray]

Compute hardswish in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#hardswish-14

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_exp

numpy_exp(x: ndarray) → Tuple[ndarray]

Compute exponential in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Exp-13

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: The exponential of the input tensor computed element-wise

function numpy_equal

numpy_equal(x: ndarray, y: ndarray) → Tuple[ndarray]

Compute equal in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Equal-11

Args:

• x (numpy.ndarray): Input tensor

• y (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_not