Usage Reference

Config file

The most important part of the library is a user-defined config yaml file. It has five separate sections: training, pruning, quantization, finetuning, and fitcompress section, currently maintained by TensorFlow only, parameters. By default, the parameters in the config are the following:

Training parameters

The following table outlines the primary parameters used to configure the training process:

Field	Type	Default	Description
epochs	int	200	Total number of training epochs.
fine_tuning_epochs	int	0	Additional epochs for fine-tuning.
pretraining_epochs	int	50	Pretraining / warm-up epochs.
rewind	str	"never"	Weight rewinding policy.
rounds	int	1	Number of prune–fine-tune cycles.
save_weights_epoch	int	-1	Save checkpoint at this epoch (-1 disables).

Note

If you require additional parameters for the training or optimization loops, please define them directly in the config.yaml file.

Quantization parameters

Field	Type	Default	Description
default_data_keep_negatives	bool	0	Default k value for data quantization (0 = clamp negatives, 1 = keep).
default_data_integer_bits	int	0	Default integer bitwidth i for data quantization.
default_data_fractional_bits	int	0	Default fractional bitwidth f for data quantization.
default_weight_keep_negatives	bool	0	Default k value for weight quantization (0 or 1).
default_weight_integer_bits	int	0	Default integer bitwidth i for weight quantization.
default_weight_fractional_bits	int	0	Default fractional bitwidth f for weight quantization.
quantize_input	bool	true	Whether inputs to layers are quantized by default.
quantize_output	bool	true	Whether outputs of layers are quantized by default.
enable_quantization	bool	true	Global switch to enable or disable quantization.
hgq_gamma	float	0.0	HGQ regularization coefficient for bitwidth stability.
hgq_beta	float	0.0	HGQ loss coefficient scaling EBOPs.
layer_specific	dict	{}	Dictionary for per-layer quantization overrides.
use_hgq	bool	false	Enable or disable High Granularity Quantization (HGQ).
use_real_tanh	bool	false	Use a real tanh instead of hard/approximate tanh.
overflow	str	"SAT"	Overflow handling mode (SAT, SAT_SYM, WRAP, WRAP_SM).
round_mode	str	"RND"	Rounding mode (TRN, RND, RND_CONV, RND_ZERO, etc.).
use_relu_multiplier	bool	true	Enable a learned bit-shift multiplier inside ReLU layers.

Fine-tuning parameters

Field	Type	Default	Description
experiment_name	str	"experiment_1"	Name of the study.
model_name	str	"resnet18"	Model architecture name.
sampler	str	GridSampler	Sampler selection for the search space.
num_trials	int	0	Number of trials.
hyperparameter_search	HyperparameterSearch	{}	Ranges for non-grid samplers.

Samplers

Field	Type	Default	Description
type	str	"TPESampler"	Sampler class name (e.g., TPESampler, GridSampler).
params	Dict[str, Any]	{}	Sampler-specific kwargs (e.g., seed, search_space).

More about samplers can be found in {optuna documentation}

HyperparameterSearch

Field	Type	Default	Description
numerical	Dict[str, List[Union[int, float]]]	{}	Numeric ranges [low, high, step].
categorical	Optional[Dict[str, List[str]]]	{}	Categorical choices.

Pruning methods

PQuantML supports seven different pruning methods.

Method Overview

Method	Model
cs	CSPruningModel
dst	DSTPruningModel
pdp	PDPPruningModel
wanda	WandaPruningModel
autosparse	AutoSparsePruningModel
activation_pruning	ActivationPruningModel
mdmm	MDMMPruningModel

There are the parameters shared by all methods:

Field	Type	Default	Description
disable_pruning_for_layers	List[str]	[]	Layer names to exclude from pruning.
enable_pruning	bool	true	Master pruning on/off switch.
threshold_decay	float	0.0	Optional pruning threshold decay term.

Note

Layer names in disable_pruning_for_layers field must match your framework’s naming (e.g., Keras layer.name).

There are more details about every pruning method:

CS Pruning

Field	Type	Default	Description
pruning_method	str	cs	Selects this pruning schema.
final_temp	int	200	Target temperature at the end of the schedule.
threshold_init	int	0	Initial sparsification threshold.

DST Pruning

Field	Type	Default	Description
pruning_method	str	dst	Selects this pruning schema.
alpha	float	5.0e-06	Mask dynamics update coefficient.
max_pruning_pct	float	0.99	Upper bound on total pruning ratio.
threshold_init	float	0.0	Initial threshold value.
threshold_type	str	"channelwise"	Thresholding granularity.

PDP Pruning

Field	Type	Default	Description
pruning_method	str	pdp	Selects this pruning schema.
epsilon	float	0.015	Smoothing/regularization factor for gating.
sparsity	float	0.8	Target sparsity level (0–1).
temperature	float	1.0e-05	Annealing temperature.
structured_pruning	bool	false	Enable structured pruning.

Wanda Pruning

Field	Type	Default	Description
pruning_method	str	wanda	Selects this pruning schema.
M	Optional[int]	null	Optional grouping constant.
N	Optional[int]	null	Optional grouping constant.
sparsity	float	0.9	Target sparsity level (0–1).
t_delta	int	100	Window size / steps for stats collection.
t_start_collecting_batch	int	100	Warm-up steps before collecting statistics.
calculate_pruning_budget	bool	true	Auto-compute pruning budget from data.

Autosparse Pruning

Field	Type	Default	Description
pruning_method	str	autosparse	Selects this pruning schema.
alpha	float	0.5	Weight/penalty coefficient.
alpha_reset_epoch	int	90	Epoch at which alpha is reset/tuned.
autotune_epochs	int	10	Number of epochs in the tuning window.
backward_sparsity	bool	false	Apply sparsity in backward pass (if supported).
threshold_init	float	-5.0	Initial threshold (often in logit space).
threshold_type	str	"channelwise"	Thresholding granularity.

Activation Pruning

Field	Type	Default	Description
pruning_method	str	activation_pruning	Selects this pruning schema.
threshold	float	0.3	Activation magnitude cutoff.
t_delta	int	50	Steps used to aggregate statistics.
t_start_collecting_batch	int	50	Steps to skip before collecting statistics.

MDMM Pruning

Field	Type	Default	Description
pruning_method	str	mdmm	Selects this pruning schema.
constraint_type	ConstraintType	"Equality"	Constraint form: equality / ≤ / ≥.
target_value	float	0.0	Target value for the chosen metric.
metric_type	MetricType	"UnstructuredSparsity"	Specifies which metric is constrained.
target_sparsity	float	0.9	Target sparsity when constraining sparsity.
rf	int	1	Regularization / frequency parameter.
epsilon	float	1.0e-03	Feasibility tolerance.
scale	float	10.0	Penalty scaling for constraint violation.
damping	float	1.0	Damping term for numerical stability.
use_grad	bool	false	Use gradient information during updates.
l0_mode	"coarse" | "smooth"	"coarse"	L0 approximation mode.
scale_mode	"mean" | "sum"	"mean"	Aggregation mode for penalties.

Optionally, there is also FITCompress method implemented for PyTorch:

FitCompress method

Field	Type	Default	Description
enable_fitcompress	bool	false	Master switch that enables or disables FITCompress.
optimize_quantization	bool	true	Whether FITCompress searches over quantization bit-width candidates.
quantization_schedule	List[float]	[7., 4., 3., 2.]	Candidate bit-widths evaluated during quantization search.
pruning_schedule	dict	{start: 0, end: -3, steps: 40}	Logarithmic pruning curve (base 10) with defined start, end, and step count.
compression_goal	float	0.10	Target compression ratio for the search procedure.
optimize_pruning	bool	false	Whether FITCompress searches over pruning ratios.
greedy_astar	bool	true	Disable fallback in A* search: once a candidate is selected, all others discarded.
approximate	bool	true	Use Fisher Trace approximations to speed up FIT score estimation.
f_lambda	float	1	Multiplicative factor λ in the distance function (g + λf).

Quantization layers in PQuantML

• PQConv*D: Convolutional layers.

• PQAvgPool*D: Average pooling layers.

• PQBatchNorm*D: BatchNorm layers.

• PQDense: Linear layer.

• PQActivation: Activation layers (ReLU, Tanh)

Note

Currently, PQuantML supports two quantization modes: layer-wise fixed-point quantization, where each tensor uses a single bit-width configuration, and High-Granularity Quantization (HGQ).