spectrans: Spectral Transformers in PyTorch

A modular library for spectral transformer implementations in PyTorch. Replaces traditional attention mechanisms with Fourier transforms, wavelets, and other spectral methods.

Features

• Modular Design: Mix and match components to create custom architectures
• Multiple Spectral Methods: FFT, DCT, DWT, Hadamard transforms, and more
• Efficient: Fast token mixing via frequency domain operations
• Type-Safe: Full type hints with Python 3.13+ support
• Well-Tested: Comprehensive test coverage
• Easy to Use: Consistent API across all models

Installation

pip install spectrans

For development:

git clone https://github.com/aaronstevenwhite/spectrans.git
cd spectrans
pip install -e ".[dev]"

Note: Windows is not currently supported. Please use Linux or macOS.

Quick Start

import torch
from spectrans.models import FNet

# Create FNet model for classification
model = FNet(
    vocab_size=30000,
    hidden_dim=768,
    num_layers=12,
    max_sequence_length=512,
    num_classes=2
)

# Forward pass with token IDs
input_ids = torch.randint(0, 30000, (2, 128))  # (batch, seq_len)
logits = model(input_ids=input_ids)
print(f"Output shape: {logits.shape}")  # torch.Size([2, 2])

# Or with embeddings directly
embeddings = torch.randn(2, 128, 768)  # (batch, seq_len, hidden_dim)
logits = model(inputs_embeds=embeddings)

Available Models

Usage Examples

Training

import torch
import torch.nn as nn
from torch.optim import AdamW
from spectrans.models import FNet

model = FNet(vocab_size=30000, hidden_dim=256, num_layers=6,
             max_sequence_length=128, num_classes=2)
optimizer = AdamW(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(3):
    input_ids = torch.randint(0, 30000, (8, 128))
    labels = torch.randint(0, 2, (8,))

    logits = model(input_ids=input_ids)
    loss = criterion(logits, labels)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    print(f"Epoch {epoch}, Loss: {loss.item():.4f}")

Using Different Models

from spectrans.models import GFNet, AFNOModel, WaveletTransformer

# Global Filter Network with learnable filters
gfnet = GFNet(vocab_size=30000, hidden_dim=512, num_layers=8,
              max_sequence_length=256, num_classes=10)

# Adaptive Fourier Neural Operator
afno = AFNOModel(vocab_size=30000, hidden_dim=512, num_layers=8,
                 max_sequence_length=256, modes_seq=32, num_classes=10)

# Wavelet Transformer
wavelet = WaveletTransformer(vocab_size=30000, hidden_dim=512,
                              num_layers=8, wavelet="db4", levels=3,
                              max_sequence_length=256, num_classes=10)

# All models share the same interface
input_ids = torch.randint(0, 30000, (4, 256))
output = gfnet(input_ids=input_ids)  # Shape: (4, 10)

Hybrid Models

from spectrans.models import HybridTransformer

# Alternate between spectral and attention layers
hybrid = HybridTransformer(
    vocab_size=30000,
    hidden_dim=768,
    num_layers=12,
    spectral_type="fourier",
    spatial_type="attention",
    alternation_pattern="even_spectral",  # Even layers use spectral
    num_heads=8,
    max_sequence_length=512,
    num_classes=2
)

output = hybrid(input_ids=input_ids)

Configuration-Based Creation

from spectrans.config import ConfigBuilder

# Load model from YAML
builder = ConfigBuilder()
model = builder.build_model("examples/configs/fnet.yaml")

# Or create programmatically
from spectrans.config.models import FNetModelConfig
from spectrans.config import build_model_from_config

config = FNetModelConfig(hidden_dim=512, num_layers=10,
                          sequence_length=128, vocab_size=8000,
                          num_classes=3)
model = build_model_from_config({"model": config.model_dump()})

Custom Components

import torch
from spectrans.layers.mixing.base import MixingLayer
from spectrans import register_component

@register_component("mixing", "my_custom_mixing")
class MyCustomMixing(MixingLayer):
    def __init__(self, hidden_dim: int):
        super().__init__(hidden_dim=hidden_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Your implementation here
        return x

    def get_spectral_properties(self) -> dict[str, str | bool]:
        """Return spectral properties of this layer."""
        return {
            "transform_type": "identity",
            "preserves_energy": True,
        }

    @property
    def complexity(self) -> dict[str, str]:
        return {"time": "O(n)", "space": "O(1)"}

# Use the custom component
custom_layer = MyCustomMixing(hidden_dim=768)
x = torch.randn(2, 128, 768)
output = custom_layer(x)

Documentation

• Full Documentation: https://spectrans.readthedocs.io
• Examples: See the examples/ directory for complete working examples
• API Reference: Available in the documentation

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Citation

If you use Spectrans in your research, please cite:

@software{spectrans,
  title = {spectrans: Modular Spectral Transformers in PyTorch},
  author = {Aaron Steven White},
  year = {2025},
  url = {https://github.com/aaronstevenwhite/spectrans}
}

License

See LICENSE for details.