aihwkit.simulator.tiles.inference module¶

High level analog tiles (inference).

class aihwkit.simulator.tiles.inference.CudaInferenceTile(*args, **kwds)¶

Bases: aihwkit.simulator.tiles.inference.InferenceTile

Analog inference tile (CUDA).

Analog inference tile that uses GPU for its operation. The instantiation is based on an existing non-cuda tile: all the source attributes are copied except for the simulator tile, which is recreated using a GPU tile.

Parameters: source_tile – tile to be used as the source of this tile

cuda(device=None)¶

Return a copy of this tile in CUDA memory.

Parameters: device (Optional[Union[torch.device, str, int]]) – CUDA device
Return type: aihwkit.simulator.tiles.inference.CudaInferenceTile

is_cuda = True¶

class aihwkit.simulator.tiles.inference.InferenceTile(*args, **kwds)¶

Bases: aihwkit.simulator.tiles.analog.AnalogTile

Tile used for analog inference and hardware-aware training for inference.

Parameters

out_size – output size
in_size – input size
rpu_config – resistive processing unit configuration.
noise_model – noise model governing the expected inference noise in a statistical manner
bias – whether to add a bias column to the tile.
in_trans – Whether to assume an transposed input (batch first)
out_trans – Whether to assume an transposed output (batch first)

cuda(device=None)¶

Return a copy of this tile in CUDA memory.

Parameters: device (Optional[Union[torch.device, str, int]]) – CUDA device
Return type: aihwkit.simulator.tiles.base.BaseTile

drift_weights(t_inference=0.0)¶

Programs and drifts the current reference weights.

The current weight reference is either the current weights or the ones at the time when initialize_drift_reference() was called, which then would overwrite the current weights with the drifted ones.

Parameters: t_inference – Time (in sec) of assumed inference time. Programming ends at t=0s. The rest is waiting time, where the devices might drift and accumulate noise. See noise model used for details.
Return type: None

forward(x_input, is_test=False)¶

Forward pass with drift compensation.

Note

The drift compensation scale will only be applied during testing, ie if is_test=True.

Parameters

x_input (torch.Tensor) –
is_test (bool) –

Return type

torch.Tensor

is_cuda = False¶

post_update_step()¶

Operators that need to be called once per mini-batch.

Return type: None

program_weights(from_reference=True)¶

Apply weights noise to the current tile weights and saves these for repeated drift experiments.

This method also establishes the drift coefficients for each conductance slice.

Parameters: from_reference – Whether to use weights from reference
Return type: None

aihwkit.simulator.tiles.inference.ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶

Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
out (Tensor, optional) – the output tensor.
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).
layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.ones(2, 3)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])

>>> torch.ones(5)
tensor([ 1.,  1.,  1.,  1.,  1.])