aihwkit.nn.modules.rnn.cells module¶

Analog cells for RNNs.

class aihwkit.nn.modules.rnn.cells.AnalogGRUCell(input_size, hidden_size, bias, rpu_config=None, realistic_read_write=False)[source]¶

Bases: aihwkit.nn.modules.container.AnalogSequential

Analog GRU Cell.

Parameters

input_size (int) – in_features size for W_ih matrix
hidden_size (int) – in_features and out_features size for W_hh matrix
bias (bool) – whether to use a bias row on the analog tile or not
rpu_config (Optional[Union[aihwkit.simulator.configs.configs.FloatingPointRPUConfig, aihwkit.simulator.configs.configs.SingleRPUConfig, aihwkit.simulator.configs.configs.UnitCellRPUConfig, aihwkit.simulator.configs.configs.InferenceRPUConfig, aihwkit.simulator.configs.configs.DigitalRankUpdateRPUConfig]]) – configuration for an analog resistive processing unit
realistic_read_write (bool) – whether to enable realistic read/write for setting initial weights and read out of weights

forward(input_, state)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters

input_ (torch.Tensor) –
state (torch.Tensor) –

Return type

Tuple[torch.Tensor, torch.Tensor]

get_zero_state(batch_size)[source]¶

Returns a zeroed state.

Parameters: batch_size (int) – batch size of the input
Returns: Zeroed state tensor
Return type: torch.Tensor

class aihwkit.nn.modules.rnn.cells.AnalogLSTMCell(input_size, hidden_size, bias, rpu_config=None, realistic_read_write=False)[source]¶

Bases: aihwkit.nn.modules.container.AnalogSequential

Analog LSTM Cell.

Parameters

input_size (int) – in_features size for W_ih matrix
hidden_size (int) – in_features and out_features size for W_hh matrix
bias (bool) – whether to use a bias row on the analog tile or not
rpu_config (Optional[Union[aihwkit.simulator.configs.configs.FloatingPointRPUConfig, aihwkit.simulator.configs.configs.SingleRPUConfig, aihwkit.simulator.configs.configs.UnitCellRPUConfig, aihwkit.simulator.configs.configs.InferenceRPUConfig, aihwkit.simulator.configs.configs.DigitalRankUpdateRPUConfig]]) – configuration for an analog resistive processing unit
realistic_read_write (bool) – whether to enable realistic read/write for setting initial weights and read out of weights

forward(input_, state)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters

input_ (torch.Tensor) –
state (Tuple[torch.Tensor, torch.Tensor]) –

Return type

Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]

get_zero_state(batch_size)[source]¶

Returns a zeroed state.

Parameters: batch_size (int) – batch size of the input
Returns: Zeroed state tensor
Return type: torch.Tensor

class aihwkit.nn.modules.rnn.cells.AnalogVanillaRNNCell(input_size, hidden_size, bias, rpu_config=None, realistic_read_write=False)[source]¶

Bases: aihwkit.nn.modules.container.AnalogSequential

Analog Vanilla RNN Cell.

Parameters

input_size (int) – in_features size for W_ih matrix
hidden_size (int) – in_features and out_features size for W_hh matrix
bias (bool) – whether to use a bias row on the analog tile or not
rpu_config (Optional[Union[aihwkit.simulator.configs.configs.FloatingPointRPUConfig, aihwkit.simulator.configs.configs.SingleRPUConfig, aihwkit.simulator.configs.configs.UnitCellRPUConfig, aihwkit.simulator.configs.configs.InferenceRPUConfig, aihwkit.simulator.configs.configs.DigitalRankUpdateRPUConfig]]) – configuration for an analog resistive processing unit
realistic_read_write (bool) – whether to enable realistic read/write for setting initial weights and read out of weights

forward(input_, state)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters

input_ (torch.Tensor) –
state (torch.Tensor) –

Return type

Tuple[torch.Tensor, torch.Tensor]

get_zero_state(batch_size)[source]¶

Returns a zeroed state.

Parameters: batch_size (int) – batch size of the input
Returns: Zeroed state tensor
Return type: torch.Tensor

class aihwkit.nn.modules.rnn.cells.LSTMState(hx, cx)¶

Bases: tuple

property cx¶: Alias for field number 1

property hx¶: Alias for field number 0

aihwkit.nn.modules.rnn.cells.sigmoid(input, *, out=None) → Tensor¶: Alias for torch.special.expit().

aihwkit.nn.modules.rnn.cells.tanh(input, *, out=None) → Tensor¶

Returns a new tensor with the hyperbolic tangent of the elements of input.

\[\text{out}_{i} = \tanh(\text{input}_{i})\]

Parameters: input (Tensor) – the input tensor.
Keyword Arguments: out (Tensor, optional) – the output tensor.

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.8986, -0.7279,  1.1745,  0.2611])
>>> torch.tanh(a)
tensor([ 0.7156, -0.6218,  0.8257,  0.2553])