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concrete.ml.pytest.torch_models.md

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

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