hls4ml.model.optimizer.passes package

Submodules

hls4ml.model.optimizer.passes.bn_fuse module

class hls4ml.model.optimizer.passes.bn_fuse.FuseBatchNormalization

Bases: OptimizerPass

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.convert_to_channels_last module

class hls4ml.model.optimizer.passes.convert_to_channels_last.ChannelsLastConverter

Bases: OptimizerPass

Converts a model from channels_first to channels_last data format by transposing the weights of relevant layers and adding a transpose layer for the inputs and outputs, if necessary

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

class hls4ml.model.optimizer.passes.convert_to_channels_last.RemoveTransposeBeforeFlatten

Bases: OptimizerPass

After the channels last conversion, model may have a sequence: Transpose -> Flatten -> Dense. In this case we can remove the expensive transpose and instead transpose the weights of the Dense layer.

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.expand_layer_group module

class hls4ml.model.optimizer.passes.expand_layer_group.ExpandLayerGroup

Bases: OptimizerPass

Expands LayerGroup (a nested model) into the parent model.

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.fuse_biasadd module

class hls4ml.model.optimizer.passes.fuse_biasadd.FuseBiasAdd

Bases: OptimizerPass

Fuses BiasAdd into Dense/Conv2D layer (common in TF models).

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.infer_precision module

class hls4ml.model.optimizer.passes.infer_precision.InferPrecisionTypes

Bases: ConfigurableOptimizerPass

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.multi_dense module

class hls4ml.model.optimizer.passes.multi_dense.ReplaceMultidimensionalDenseWithConv

Bases: OptimizerPass

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.nop module

class hls4ml.model.optimizer.passes.nop.EliminateLinearActivation

Bases: OptimizerPass

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.precision_merge module

class hls4ml.model.optimizer.passes.precision_merge.SetPrecisionConcat

Bases: OptimizerPass

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Set concat output precision

hls4ml.model.optimizer.passes.precision_merge.get_concat_type(itype1, itype2)

hls4ml.model.optimizer.passes.qkeras module

class hls4ml.model.optimizer.passes.qkeras.ApplyAlpha(model, name, attributes, inputs, outputs=None)

Bases: BatchNormalization

A custom layer to scale the output of a QDense layer which used ‘alpha != 1’ Inference computation uses BatchNormalization methods

add_bias(bias, quantizer=None)
add_weights(scale, quantizer=None)
initialize()
class hls4ml.model.optimizer.passes.qkeras.ExtractTernaryThreshold

Bases: OptimizerPass

The input value (threshold) at which the output of a a ternary activation changes is configurable. This pass extracts that threshold point, inserting a BatchNormalization layer to execute the scaling. That BatchNormalization layer is then expected to be fused into a BatchNormalizationQuantizedTanh layer configured with the correct threshold.

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

class hls4ml.model.optimizer.passes.qkeras.FuseConsecutiveBatchNormalization

Bases: OptimizerPass

OptimizerPass to merge consecutive BatchNormalization layers. These may exist in a model after QKerasFactorizeAlpha layer. Scale and Bias of each layer are combined into scale and bias of a single layer.

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

class hls4ml.model.optimizer.passes.qkeras.OutputRoundingSaturationMode

Bases: ConfigurableOptimizerPass

Set the Rounding and Saturation mode of the output (and accumulator, if applicable) of the layers specific in layer list. The layer list is empty by default. To specify which layer to apply this pass to, perform e.g.: hls4ml.model.optimizer.get_optimizer(‘output_rounding_saturation_mode’).configure(layers=[‘Dense’, ‘Activation’]) The Rounding and Saturation modes are ‘None’ by default (so use the compiler defaults) To set which mode to use: hls4ml.model.optimizer.get_optimizer(‘output_rounding_saturation_mode’).configure(rounding_mode=’AP_RND_CONV’) hls4ml.model.optimizer.get_optimizer(‘output_rounding_saturation_mode’).configure(saturation_mode=’AP_SAT’)

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

precision_string_modify(pstr)
transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

class hls4ml.model.optimizer.passes.qkeras.QKerasFactorizeAlpha

Bases: OptimizerPass

OptimizerPass for extracting alpha “scale” from QKeras quantized layer. The weights of the Q{Dense, Conv} layer are scaled to the common data type, and an ‘ApplyAlpha’ layer is inserted to reapply the scale.

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.qkeras.register_qkeras()

hls4ml.model.optimizer.passes.stamp module

class hls4ml.model.optimizer.passes.stamp.MakeStamp

Bases: ModelOptimizerPass

transform(model)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

hls4ml.model.optimizer.passes.transpose_opt module

class hls4ml.model.optimizer.passes.transpose_opt.RemoveNopTranspose

Bases: OptimizerPass

Remove a transpose layer if it doesn’t do anything to a 1D array. i.e, 1D input and perm = [0]

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

class hls4ml.model.optimizer.passes.transpose_opt.RemoveSingleChannelTranspose

Bases: OptimizerPass

Remove transpose of inputs if the number of channels is 1 as for io_parallel this doesn’t affect the array representation used

match(node)

Predicate to match on a given node.

Parameters:

node (Layer) – Node in the model graph to try matching the optimizer on.

transform(model, node)

Transformation to apply if matching was successful.

Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).

Parameters:

  • model (ModelGraph) – Model to optimize

  • node (Layer) – The matched node in the model graph.

Module contents