aihwkit.simulator.tiles.base module¶

High level analog tiles (base).

class aihwkit.simulator.tiles.base.AnalogTileStateNames[source]

Bases: object

Class defining analog tile state name constants.

Caution

Do not edit. Some names are attribute names of the tile.

CLASS = 'analog_tile_class'

CONTEXT = 'analog_ctx'

HIDDEN_PARAMETERS = 'analog_tile_hidden_parameters'

HIDDEN_PARAMETER_NAMES = 'analog_tile_hidden_parameter_names'

LR = 'analog_lr'

MAPPING_SCALES = 'mapping_scales'

OUT_SCALING = 'out_scaling_alpha'

RPU_CONFIG = 'rpu_config'

SHARED_WEIGHTS = 'shared_weights'

WEIGHTS = 'analog_tile_weights'

class aihwkit.simulator.tiles.base.BaseTile(out_size, in_size, rpu_config, bias=True, in_trans=False, out_trans=False)[source]

Bases: Generic[aihwkit.simulator.tiles.base.RPUConfigGeneric]

Base class for tiles.

Parameters

out_size – output size
in_size – input size
rpu_config – resistive processing unit configuration.
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)

apply_input_range(values, update_from_data=False)[source]

Apply the input clipping.

Parameters

values (torch.Tensor) – tensor to clip
update_from_data (bool) – whether to update from data if applicable

Returns

clipped output tensor

Return type

torch.Tensor

apply_out_scaling(values, tensor_view=None)[source]

Apply the learned out scaling to the given tensor.

Parameters

values (torch.Tensor) – tensor to apply scaling to.
tensor_view (Optional[Tuple[int, ...]]) – view to cast the out scalings before multiplication

Returns

output tensor with applied out scaling factors

Return type

torch.Tensor

apply_weight_scaling(combined_weights, weight_scaling_omega=None)[source]

Set the tile weights (and biases) in a scaled fashion.

Scales the weights by a layerwise scale or columnwise scale (if weight_scaling_columnwise is set), that is then applied in digital at the output of forward and backward pass, and the learning rate for this tile is adjusted accordingly.

If layerwise scale is chosen, weights are scaled by \(\omega/\max_{ij} |w_{ij}|\) and the global digital factor \(alpha\) is set to \(\max_{ij} |w_{ij}|/\omega\).

It can be shown that such a constant factor greatly improves the SNR and training accuracy as the full weight range of the analog devices are used. See also Rasch, Gokmen & Haensch (2019) for more details.

Parameters

combined_weights (torch.Tensor) – [d_size, x_size] weight matrix.
weight_scaling_omega (Optional[float]) – where the weight max should be mapped in terms of the weight range. Note that for omega larger than the maximal weight of the device, weights will get clipped for most devices. If this parameter is not given, it will default to the weight_scaling_omega value set in the MappingParameter of the rpu_config

Returns

scaled weights.

Return type

torch.Tensor

backward(d_input, ctx=None)[source]

Perform the backward pass.

Parameters

d_input (torch.Tensor) – [N, out_size] tensor. If out_trans is set, transposed.
ctx (Any) – torch auto-grad context [Optional]

Returns

[N, in_size] tensor. If in_trans is set, transposed.

Return type

torch.Tensor

backward_indexed(d_input, ctx=None)[source]

Perform the backward pass for convolutions.

Depending on the input tensor size it performs the backward pass for a 2D image or a 3D one.

Parameters

d_input (torch.Tensor) – [N, out_size] tensor. If out_trans is set, transposed.
ctx (Any) – torch auto-grad context [Optional]

Returns

[N, in_size] tensor. If in_trans is set, transposed.

Return type

torch.Tensor

Raises

TileError – if the indexed tile has not been initialized, or if self.images_sizes does not have a valid dimennion.

cpu()[source]

Return a copy of this tile in CPU memory.

Returns: self in case of CPU
Return type: aihwkit.simulator.tiles.base.BaseTile

cuda(device=None)[source]

Return a copy of this tile in CUDA memory.

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

decay_weights(alpha=1.0)[source]

Decays the weights once according to the decay parameters of the tile.

Parameters: alpha (float) – additional decay scale (such as LR). The base decay rate is set during tile init.
Returns: None.
Return type: None

diffuse_weights()[source]

Diffuses the weights once according to the diffusion parameters of the tile.

The base diffusion rate is set during tile init.

Returns: None
Return type: None

drift_weights(delta_t=1.0)[source]

Drifts the weights once according to the drift parameters of the tile.

See also DriftParameter.

Parameters: delta_t (float) – Time since last drift call.
Returns: None.
Return type: None

ensure_shared_weights(shared_weights=None)[source]

Ensure that the shared_weights is set properly.

Caution

This is only called from analog function.

No-op if shared weights is not used.

Parameters: shared_weights (Optional[torch.Tensor]) –
Return type: None

forward(x_input, is_test=False, ctx=None)[source]

Perform the forward pass.

Calls first the pre_forward, then the tile forward, and finally the post_forward step.

Note

The full forward pass is not using autograd, thus all pre and post functions need to be handled appropriately in the pre/post backward functions.

Parameters

x_input (torch.Tensor) – [N, in_size] tensor. If in_trans is set, transposed.
is_test (bool) – whether to assume testing mode.
ctx (Any) – torch auto-grad context [Optional]

Returns

[N, out_size] tensor. If out_trans is set, transposed.

Return type

torch.Tensor

forward_indexed(x_input, is_test=False, ctx=None)[source]

Perform the forward pass for convolutions.

Depending on the input tensor size it performs the forward pass for a 2D image or a 3D one.

Parameters

x_input (torch.Tensor) – [N, in_size] tensor. If in_trans is set, transposed.
is_test (bool) – whether to assume testing mode.
ctx (Any) – torch auto-grad context [Optional]

Returns

[N, out_size] tensor. If out_trans is set, transposed.

Return type

torch.Tensor

Raises

TileError – if the indexed tile has not been initialized, or if self.images_sizes does not have a valid dimennion.

get_analog_ctx()[source]

Return the analog context of the tile to be used in AnalogFunction.

Return type: aihwkit.optim.context.AnalogContext

get_brief_info()[source]

Return short info about the underlying C++ tile.

Return type: str

get_hidden_parameters()[source]

Get the hidden parameters of the tile.

Returns: Ordered dictionary of hidden parameter tensors.
Return type: collections.OrderedDict

get_hidden_update_index()[source]

Get the current updated device index of the hidden devices.

Usually this is 0 as only one device is present per cross-point for many tile RPU configs. However, some RPU configs maintain internally multiple devices per cross-point (e.g. VectorUnitCell).

Returns: The next mini-batch updated device index.
Return type: int

Note

Depending on the update and learning policy implemented in the tile, updated devices might switch internally as well.

get_learned_out_scales()[source]

Get the learned_out_scaled that can be used add an output scale to the weights, that is learned.

Returns: learned_out_scales
Return type: tensor

get_learning_rate()[source]

Return the tile learning rate.

Returns: the tile learning rate.
Return type: float

get_mapping_scales()[source]

Get the scales used for the weight mapping.

Returns: the vector (or scalar) that is used to determine the mapping into (norm) conductance units. These scales are used at the output of the analog MVM.
Return type: Mapping scales

get_scales()[source]

Set all scales with a new scale.

Returns: Scale tensor if any scale exist else None.
Return type: Optional[torch.Tensor]

get_weights(apply_weight_scaling=False)[source]

Get the tile weights (and biases).

Gets the tile weights and extracts the mathematical weight matrix and biases (if present, by determined by the self.bias parameter).

Note

The returned weight is a copy of the internal weights (not a pointer) and is always on CPU and detached.

Note

This is not a hardware realistic weight readout. Use

get_weights_realistic() for a realistic transfer.

Parameters: apply_weight_scaling (bool) – Whether to return the weights with the (digital) output scaling factors applied. Note the “logical” weights of the layer which the DNN is effectively using are those with the output scales applied. If apply_weight_scaling is set to False, then only the weight values that is programmed onto the crossbar array are returned, without applying the digital scales.
Returns: a tuple where the first item is the [out_size, in_size] weight matrix; and the second item is either the [out_size] bias vector or None if the tile is set not to use bias.
Return type: Tuple[torch.Tensor, Optional[torch.Tensor]]

get_weights_realistic(apply_weight_scaling=False)[source]

Get the tile weights (and biases) in a realistic manner by using the forward pass for weights readout.

Gets the tile weights and extracts the mathematical weight matrix and biases (if present, by determined by the self.bias parameter).

Note

The returned weight is a copy of the internal weights (not a pointer) and is always on CPU and detached.

Parameters: apply_weight_scaling (bool) – Whether to return the weights with the (digital) output scaling factors applied. Note the “logical” weights of the layer which the DNN is effectively using are those with the output scales applied. If apply_weight_scaling is set to False, then only the weight values that is programmed onto the crossbar array are returned, without applying the digital scales.
Returns: a tuple where the first item is the [out_size, in_size] weight matrix; and the second item is either the [out_size] bias vector or None if the tile is set not to use bias.
Return type: Tuple[torch.Tensor, Optional[torch.Tensor]]

init_input_processing()[source]

Helper function to initialize the input processing.

Note

This method is called from the constructor.

Raises: ConfigError in case manage_output_clipping is: enabled but not supported.

Return type: None

init_learned_out_scales()[source]

Helper function to initialize the learned out scaling used to scale the weights in digital.

Note

This method is called from the constructor.

Return type: None

init_mapping_scales()[source]

Helper function to initialize the mapping scales used to scale the weights in digital and determine the conductance conversion.

Note

This method is called from the constructor.

Return type: None

is_indexed()[source]

Returns whether index matrix for convolutions has been set.

Returns: Whether index matrix has been set
Return type: bool

post_backward(d_output, dim, ctx=None)[source]

Operations after the actual backward step for post processing.

Here, the mapping scales are applied if exist.

Parameters

d_output (torch.Tensor) – The output tensor from the analog MVM of the tile.
dim (int) – the dim of the x_size dimension
ctx (Any) – torch auto-grad context [Optional]

Returns

The postprocessed tensor of the same shape

Return type

torch.Tensor

post_forward(x_output, dim, is_test=False, ctx=None)[source]

Operations after the actual forward step for post processing.

Parameters

x_output (torch.Tensor) – tensor that is the output from the forward pass of the tile
dim (int) – output channel dimension, ie the d_size dimension
is_test (bool) – whether in eval mode
ctx (Any) – torch auto-grad context [Optional]

Returns

Output tensor of the same shape

Return type

torch.Tensor

post_update_step()[source]

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

Note

This function is called by the analog optimizer.

Caution

If no analog optimizer is used, the post update steps will not be performed.

Return type: None

pre_backward(d_input, dim, ctx=None)[source]

Operations before the actual backward step for pre processing.

By default, this is an no-op. However, it could be overridden in derived tile classes.

Parameters

d_input (torch.Tensor) – The input tensor from to the analog MVM of the tile.
dim (int) – the dim of the d_size dimension
ctx (Any) – torch auto-grad context [Optional]

Returns

The preprocessed tensor of the same shape

Return type

torch.Tensor

pre_forward(x_input, dim, is_test=False, ctx=None)[source]

Operations before the actual forward step for pre processing.

By default, this is an no-op. However, it could be overridden in derived tile classes.

Parameters

x_input (torch.Tensor) – input tensor for the analog MVM of the tile.
dim (int) – input channel dimension, ie the x_size dimension
is_test (bool) – whether in eval mode
ctx (Any) – torch auto-grad context [Optional]

Returns

Output tensor of the same shape

Return type

torch.Tensor

pre_update(x_input, x_dim, d_input, d_dim)[source]

Operations before the actual update step for pre processing.

Be default, if the mapping scales are used, the d_input will be divided by the mapping scales to compensate for the conductance mapping.

Caution

The x_input and d_input here are the original inputs to the forward` and ``backward methods, thus the pre_forward and pre_backward function are not applied, and might need to be applied again here.

Parameters

x_input (torch.Tensor) – The forward input tensor.
x_dim (int) – the dim of the x_size dimension of the forward input.
d_input (torch.Tensor) – The backward (gradient) input tensor.
d_dim (int) – the dim of the d_size dimension of the backward input.

Returns

Tuple of the preprocessed x_input and d_input tensors of the same shape

Return type

Tuple[torch.Tensor, torch.Tensor]

reset(reset_prob=1.0)[source]

Reset the updated device tile according to the reset parameters of the tile.

Resets the weights with device-to-device and cycle-to-cycle variability (depending on device type), typically:

\[W_{ij} = \xi*\sigma_\text{reset} + b^\text{reset}_{ij}\]

The reset parameters are set during tile init.

Parameters: reset_prob (float) – individual probability of reset.
Returns: None
Return type: None

reset_columns(start_column_idx=0, num_columns=1, reset_prob=1.0)[source]

Reset (a number of) columns according to the reset parameters of the tile.

Resets the weights with device-to-device and cycle-to-cycle variability (depending on device type), typically:

\[W_{ij} = \xi*\sigma_\text{reset} + b^\text{reset}_{ij}\]

The reset parameters are set during tile init.

Parameters

start_column_idx (int) – a start index of columns (0..x_size-1)
num_columns (int) – how many consecutive columns to reset (with circular warping)
reset_prob (float) – individual probability of reset.

Returns

None

Return type

None

reset_delta_weights()[source]

Reset the weight grad tensor to default update behavior (i.e. adding the update directly to the weight).

No-op if shared weights is not used.

Return type: None

set_delta_weights(delta_weights=None)[source]

Set the weight grad tensor and set the update to.

No-op if shared weights is not used.

Parameters: delta_weights (Optional[torch.Tensor]) –
Return type: None

set_hidden_parameters(ordered_parameters)[source]

Set the hidden parameters of the tile.

Caution

Usually the hidden parameters are drawn according to the parameter definitions (those given in the RPU config). If the hidden parameters are arbitrary set by the user, then this correspondence might be broken. This might cause problems in the learning, in particular, the weight granularity (usually dw_min, depending on the device) is needed for the dynamic adjustment of the bit length (update_bl_management, see UpdateParameters).

Currently, the new dw_min parameter is tried to be estimated from the average of hidden parameters if the discrepancy with the dw_min from the definition is too large.

Parameters: ordered_parameters (collections.OrderedDict) – Ordered dictionary of hidden parameter tensors.
Raises: TileError – In case the ordered dict keys do not conform with the current rpu config tile structure of the hidden parameters
Return type: None

set_hidden_update_index(index)[source]

Set the current updated hidden device index.

Usually this is ignored and fixed to 0 as only one device is present per cross-point. Other devices, might not allow explicit setting as it would interfere with the implemented learning rule. However, some tiles have internally multiple devices per cross-point (eg. unit cell) that can be chosen depending on the update policy.

Parameters: index (int) – device index to be updated in the next mini-batch
Return type: None

Note

Depending on the update and learning policy implemented in the tile, updated devices might switch internally as well.

set_indexed(indices, image_sizes)[source]

Set the index matrix for convolutions and switches to indexed forward/backward/update versions.

Parameters

indices (torch.Tensor) – torch.tensor with int indices
image_sizes (List) – [C_in, H_in, W_in, H_out, W_out] sizes

Raises

ValueError – if image_sizes does not have valid dimensions.
TileError – if the tile uses transposition.

Return type

None

set_learned_out_scales(alpha)[source]

Helper function to set the out scaling alpha used to scale the weights in digital.

Note

Will be a no-op in case init_learned_out_scales() was not called

Caution

Will not check the correct size of the given alpha.

Parameters: alpha (Union[torch.Tensor, float]) – out scales as a parameter that is learned.
Return type: None

set_learning_rate(learning_rate)[source]

Set the tile learning rate.

Set the tile learning rate to -learning_rate. Note that the learning rate is always taken to be negative (because of the meaning in gradient descent) and positive learning rates are not supported.

Parameters: learning_rate (float) – the desired learning rate.
Returns: None.
Return type: None

set_mapping_scales(mapping_scales)[source]

Set the scales used for the weight mapping.

Parameters

mapping_scales (Optional[Union[torch.Tensor, float]]) – Vector (or scalar) used for the mapping
in (of weights into conductance units. This mapping is never) –
when (the SGD graph but might get initialized) –
enforced. (weight_scaling_omega is used or remapping is) –

Return type

None

set_scales(scales)[source]

Set all scales with a new scale.

This will set the mapping scales to scales and set all other scales to 1.

Parameters: scales (Union[torch.Tensor, float]) – scales to set.
Return type: None

set_weights(weights, biases=None, apply_weight_scaling=False, weight_scaling_omega=None)[source]

Set the tile weights (and biases).

Sets the internal tile weights to the specified values, and also the internal tile biases if the tile was set to use bias (via self.bias).

Note

This setting is not hardware realistic. Use the set_weights_realistic() for a realistic weight transfer.

Parameters

weights (torch.Tensor) – [out_size, in_size] weight matrix.
biases (Optional[torch.Tensor]) – [out_size] bias vector. This parameter is required if self.bias is True, and ignored otherwise.
apply_weight_scaling (bool) – Whether to rescale the given weight matrix and populate the digital output scaling factors as specified in the configuration MappingParameter. A new weight_scaling_omega can be given. Note that this will overwrite the existing digital out scaling factors.
weight_scaling_omega (Optional[float]) – The weight scaling omega factor (see MappingParameter). If given explicitly here, it will overwrite the value in the mapping field.

Returns

None.

Return type

None

set_weights_realistic(weights, biases=None, apply_weight_scaling=False, weight_scaling_omega=None, n_loops=10)[source]

Set the tile weights (and biases) in a realistic manner by using the forward and update pass.

Sets the internal tile weights to the specified values, and also the internal tile biases if the tile was set to use bias (via self.bias).

Parameters

weights (torch.Tensor) – [out_size, in_size] weight matrix.
biases (Optional[torch.Tensor]) – [out_size] bias vector. This parameter is required if self.bias is True, and ignored otherwise.
apply_weight_scaling (bool) – Whether to rescale the given weight matrix and populate the digital output scaling factors as specified in the configuration MappingParameter. A new weight_scaling_omega can be given. Note that this will overwrite the existing digital out scaling factors.
weight_scaling_omega (Optional[float]) – The weight scaling omega factor (see MappingParameter). If given explicitly here, it will overwrite the value in the mapping field.
n_loops (int) – number of times the columns of the weights are set in a closed-loop manner. A value of 1 means that all columns in principle receive enough pulses to change from w_min to w_max.

Returns

None.

Raises

ValueError – if the tile has bias but bias has not been specified.

Return type

None

update(x_input, d_input)[source]

Perform the update pass.

Calls the pre_update method to pre-process the inputs.

Parameters

x_input (torch.Tensor) – [..., in_size] tensor. If in_trans is set, [in_size, ...].
d_input (torch.Tensor) – [..., out_size] tensor. If out_trans is set, [out_size, ...].

Returns

None

Return type

None

update_indexed(x_input, d_input)[source]

Perform the update pass for convolutions.

Calls the pre_update methods to pre-process the inputs.

Parameters

x_input (torch.Tensor) – [N, in_size] tensor. If in_trans is set, transposed.
d_input (torch.Tensor) – [N, out_size] tensor. If out_trans is set, transposed.

Returns

None

Return type

None