torch-weighttracker 0.1.3

Tools for tracking structured weight sparsity in PyTorch models.

torch-weighttracker

Tools for tracking structured weight sparsity, regularization signals, and bit-operation estimates in PyTorch models.

torch-weighttracker is useful when the question is not "what is in this one tensor?" but "what is happening to the coupled channel, head, or feature unit that is shared across several tensors?"

The package builds a structural view of a model, compiles tensorized reduction plans over that structure, and reuses those plans for training-time metrics and regularizers.

import torch
from torch import nn

from torch_weighttracker import WeightTracker

model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
tracker = WeightTracker(model, example_inputs=torch.randn(1, 4))

print(tracker.view_structures())

Installation

python -m pip install torch-weighttracker

Structured BOPs MAC accounting uses fvcore for baseline per-module MACs:

python -m pip install "torch-weighttracker[structured-bops]"

Why Use It?

PyTorch makes it easy to inspect one parameter tensor at a time. Structured compression often needs a different view:

• A channel can be coupled across convolutions, batch norms, linear layers, and residual paths.
• A transformer unit can mean an attention head, a head dimension, or a fused QKV slice rather than a simple row or column.
• A metric such as "active BOPs" depends on sparsity, module shape, MAC counts, and bitrates at the same time.
• A regularizer such as group lasso should penalize the coupled structural unit, not each weight tensor independently.

WeightTracker turns those coupled structures into canonical units, then lets calculations operate over the canonical units with reusable tensor programs.

Use Cases

Current high-value use cases:

• Add structured group lasso to a training loss.
• Track active structured BOPs and compression rate during structured pruning, sparsity-aware training, or quantization-aware training (QAT).
• Inspect which modules participate in each channel, feature, head, or head-dim group.
• Build structural metrics that aggregate many weight tensors into one value per pruning unit.

Group Lasso

Structured group lasso regularizes coupled units together. Layers can be excluded per regularizer:

from torch_weighttracker.regularizers import RegularizerType

group_lasso = tracker.create_regularizer(
    RegularizerType.GROUP_LASSO,
    ignore=[model.classifier],
)

loss = task_loss + 1e-4 * group_lasso()
loss.backward()

Structured BOPs

Structured BOPs compares active bit operations against a dense 32-bit baseline:

import torch

from torch_weighttracker.trackers import TrackerType

metrics = tracker.create_tracker(
    TrackerType.STRUCTURED_BOPS,
    ignore=[torch.nn.BatchNorm2d],
    log_compression_rate=True,
).track()

print(metrics["structured_bops"])
print(metrics["structured_bops_baseline"])
print(metrics["structured_bops_compression_rate"])

Architecture

The main API is WeightTracker. Internally it is split into a few layers:

1. Dependency discovery: WeightTracker builds dependency groups from the model and example_inputs, or accepts precomputed groups.
2. Canonical units: canonical_units.py normalizes raw dependency groups into CanonicalUnitGroup objects. These give channels, features, attention heads, and head dimensions a shared unit index.
3. Reduction plans: reductions/ and plans/ compile module and unit mappings into segment and index operations that use PyTorch's efficient tensor computations.
4. Calculations: calculations/ defines named calculation specs such as per-unit L2 norm, active units, parameters per unit, active MACs, and bitrates. Calculations can depend on each other and cache constant results.
5. Consumers: regularizers/ and trackers/ request the calculations they need, optionally with an ignore context for excluding modules from a specific metric or regularizer.

The result is a small public surface with a reusable internal graph:

model + example inputs
        |
        v
dependency groups -> canonical units -> reduction plans -> calculations
                                                          |
                                                          v
                                               regularizers and trackers

Speed

Compared with a naive implementation, the current implementation gives the following speedups:

• Group lasso: 15.503x
  • Naive: 4.6540s total, 232.698ms/step
  • WeightTracker: 0.3002s total, 15.010ms/step
• Structured BOPs: 2.531x
  • Naive: 0.6757s total, 33.783ms/step
  • WeightTracker: 0.2669s total, 13.346ms/step

Comparison	Speedup	Naive extra allocation	WeightTracker extra allocation
Group lasso	15.421x	197.0MiB	197.0MiB
Structured BOPs	2.582x	1.7GiB	195.9MiB

Status

This package is pre-1.0. Public APIs may still change while the tracker, calculation, and regularizer surfaces settle.

Future Work

1. Streamline definitions and method names across the codebase.
2. Improve calculation caching so repeated computations are not performed twice.
3. Improve compilation of computation plans for bigger speedups.
4. Improve memory management within calculations.
5. Write more comprehensive docstrings.

Future custom use cases will need a broader top-level WeightTracker API for custom operations, custom layers, and generic group definitions.

License

MIT